JP2025108598A - Multimodal Feedback Integrated Knowledge Retrieval Enhanced Robot Control System - Google Patents

Abstract

To realize improvement of complicated task execution ability under unpredictable circumstance, understanding and execution of high-level abstract instructions, effective integration of multi-modal feedback, enhancement of ability to adapt itself to change of environment and uncertainty, efficient use of knowledge base, and securing expandability.SOLUTION: The present invention is directed to a robot control system which integrates a large-scale language model, retrieval-enhanced generation technique, a visual system, a force sense module, a voice recognition and synthesizing module, a multi-modal integration module, and a robot control system. A language processing component recognizes high-level instructions, and dynamically selects and adapts relevant examples from a knowledge base by using the retrieval-enhanced generation technique. The visual system generates a three-dimensional expression of an environment. The force sense module measures force of an end effector. The multi-modal integration module integrates information from different modalities to realize environment understanding and adaptive action generation. Thus, it is possible to adapt the system to execution of complicated tasks and change of the environment under an unpredictable environment.SELECTED DRAWING: None

Description

The present invention relates to the fields of artificial intelligence and robotics, and in particular to a robot control system that integrates visual and haptic feedback using large-scale language modeling (LLM) and retrieval-enhanced generation (RAG) techniques. More specifically, it relates to the realization of a robot system that can understand high-level instructions in human natural language and perform complex long-term tasks in uncertain and unpredictable environments. Inspired by the concept of "embodied cognition" in human cognitive processes, the present invention improves the adaptability and intelligence of robots by integrating language processing capabilities with sensory-motor feedback.

In recent years, the fields of artificial intelligence and robotics have required robots to improve their ability to perform complex tasks. Conventional robotic systems rely on pre-programmed responses and have limited ability to adapt to changes and uncertainties in the environment. In particular, performing complex tasks in unpredictable environments such as homes and medical settings has been difficult. For example, even a seemingly simple task such as "making coffee" requires dealing with many subtasks and environmental uncertainties, such as locating the mug, opening and closing drawers, scooping coffee beans, and adjusting the amount of hot water poured.

Human intelligence is often understood as "embodied cognition," where cognitive processes such as attention, language, learning, memory, and perception are intrinsically linked to how the body interacts with the surrounding environment. According to the work of Lakoff & Johnson (1999), the human conceptual system is rooted in bodily experience, and even abstract thought is constructed through bodily metaphors. Wilson (2002) also argues that cognition is formed through interactions with the environment and is not limited to abstract information processing in the brain. Furthermore, Shapiro (2011) shows that body morphology and motor capabilities affect cognitive processes. These studies provide evidence that human intelligence is based on sensory-motor processes, which has important implications for the design of machine intelligence.

In the past, the development of robot sensory-motor capabilities and artificial intelligence has progressed in parallel, but attempts to effectively integrate them have been limited. On the one hand, research has focused on improving the sensory-motor capabilities of robots, and technologies such as object recognition and grasping using visual feedback (Levine et al., 2018) and precise manipulation using force feedback (Hogan, 1985; Siciliano & Khatib, 2016) have been developed. On the other hand, in the field of artificial intelligence, the development of large-scale language models (LLMs) has led to improvements in natural language understanding, generation, and inference capabilities (Brown et al., 2020; Devlin et al., 2019). However, the development of a robot system that integrates these technologies and combines language understanding capabilities with sensory-motor feedback has faced many technical challenges and has not progressed sufficiently.

Approaches such as reinforcement learning and imitation learning have been shown to be effective in performing complex tasks. For example, research has been conducted on robot motion control using reinforcement learning (Schulman et al., 2017; Haarnoja et al., 2018) and skill acquisition using imitation learning (Finn et al., 2016; Ho & Ermon, 2016). However, these approaches have challenges in adapting to new tasks and diverse scenarios. Reinforcement learning requires large amounts of data and trial and error, and learning in the real world is time-consuming and resource-intensive. In addition, imitation learning relies on human demonstration, so it can be difficult to generalize to new situations.

In recent years, research on robot control using large-scale language models (LLMs) has progressed. For example, VoxPoser (Huang et al., 2023) leverages LLMs to perform everyday manipulation tasks, and Robotics Transformer (RT-2) (Brohan et al., 2023) has demonstrated adaptive capabilities to perform tasks beyond training scenarios by leveraging large-scale web data and robot learning data. In addition, the hierarchical diffusion policy (Chi et al., 2023) introduces a model structure that generates context-aware motion trajectories that enhance task-specific motion from high-level LLM decision inputs.

However, these approaches have challenges, such as complex prompt requirements, lack of real-time feedback, underutilization of haptic feedback, and inefficient pipelines that hinder task execution. In particular, in robot control using LLM, prompt design is complex, and desired results are often not achieved unless appropriate instructions are given. In addition, there is a lack of feedback mechanisms to respond to environmental changes in real time, limiting the ability to adapt to unpredictable situations. Furthermore, many approaches have focused on visual feedback and underutilized haptic feedback. This limits the ability to adapt to physical interactions that cannot be captured by visual information alone (e.g., adjusting the amount of liquid injected or adjusting the force required to open and close a door).

In addition, the application of retrieval-enhanced generation (RAG) technology to robotics has not been fully explored, despite its potential. RAG is a technique that retrieves relevant information from an external knowledge base and incorporates it into the generation process to improve the output of a language model (Lewis et al., 2020). This technique has been shown to reduce hallucinations in language models and generate more accurate and reliable outputs. However, the application of RAG to robot control, especially to motion generation for complex task execution, has been limited.

In light of these circumstances, there is a demand for the development of robotic systems that can effectively integrate language comprehension capabilities with sensory-motor feedback to enable the execution of complex tasks in unpredictable environments. To address these challenges, the present invention provides a robotic control system that integrates large-scale language models (LLMs), retrieval-enhanced generation (RAG) technology, and visual and haptic feedback.

Mon-Williams, R., Li, G., Long, R. et al. Embodied large language models enable robots to complete complex tasks in unpredictable environments. Nat Mach Intell (2025). https://doi.org/10.1038/s42256-025-01005-x

The present invention aims to solve the following problems:

1. Improved ability to perform complex tasks in unpredictable environments:
Conventional robotic systems rely on pre-programmed responses and have limited adaptability to environmental changes and uncertainties. In particular, performing complex tasks in unpredictable environments such as the home or medical settings is difficult. The present invention aims to improve the ability to perform complex tasks while adapting to environmental changes and uncertainties.

2. Understanding and executing high-level abstract instructions:
Conventional robot systems require specific operation instructions, and it is difficult for them to understand abstract instructions such as "make coffee" and break them down into appropriate subtasks for execution. The present invention aims to improve the ability to understand high-level abstract instructions in human natural language, break them down into appropriate subtasks, and execute them.

3. Effective integration of visual and haptic feedback:
Conventional robot systems have focused on visual feedback and have underutilized haptic feedback. In addition, mechanisms for integrating and utilizing these feedbacks are limited. The present invention aims to effectively integrate visual and haptic feedback to improve the accuracy and adaptability of interaction with the environment.

4. Strengthening adaptability to environmental changes and uncertainties:
Conventional robot systems have limited adaptability to environmental changes and uncertainties, making it difficult to respond to unexpected situations (e.g., movement of objects, appearance of obstacles). The present invention aims to incorporate real-time feedback to enhance adaptability to environmental changes and uncertainties.

5. Ensuring efficient use and scalability of the knowledge base:
In conventional robot systems, the use of knowledge bases is limited, making it difficult to add or update new knowledge. In addition, performance degradation as the knowledge base expands is an issue. The present invention aims to use Retrieval Augmented Generation (RAG) technology to efficiently utilize the knowledge base and ensure the scalability of the system.

6. Planning and executing long-term tasks:
Conventional robot systems focus on the execution of short-term tasks, and have difficulty planning and executing long-term tasks consisting of multiple subtasks. The present invention aims to improve the ability to plan and execute long-term tasks that take into account the dependencies between subtasks.

7. Creative movement generation:
Conventional robot systems rely on pre-programmed motion patterns and are limited in their ability to generate creative motions in response to new situations or requirements. The present invention aims to improve the ability to generate new motion patterns by combining a language model and a visual generation model.

8. Ensuring safety and reliability:
As robot systems become more sophisticated, ensuring safety and reliability has become an important issue. The present invention aims to incorporate safety constraints to ensure the safety and reliability of the system.

9. Multimodal information integration and processing:
Conventional robotic systems have limited ability to effectively integrate and process information from different modalities (such as vision, force, language, etc.). The purpose of this invention is to integrate multimodal information to realize richer environmental understanding and adaptive behavior generation.

10. Natural human interaction:
Conventional robot systems have limited ability to interact naturally with humans. The purpose of the present invention is to realize natural interaction with humans through natural language understanding and generation, and recognition and generation of non-verbal communication (gestures, facial expressions, etc.).

By solving these problems, the present invention aims to contribute to the realization of "intelligent robots" that can cooperate with humans and perform complex tasks.

The present invention provides a multimodal feedback integrated knowledge retrieval enhanced robot control system (hereinafter referred to as the system), which integrates a large-scale language model (LLM), a retrieval enhanced generation (RAG) infrastructure, a vision system, a haptic module, a speech recognition and synthesis module, and a robot control system.

The main components of the system are:

1. Language processing component:
The language processing component is responsible for processing user queries and environmental data, and breaking down complex tasks into a series of actionable steps. This component uses large-scale language models (e.g., GPT-4) for natural language understanding and generation, task decomposition and planning, and code generation. It also uses Search Augmented Generative (RAG) techniques to dynamically select and adapt to relevant examples from a curated knowledge base.

The main functions of the language processing component are:
- Natural language understanding and interpretation: understanding user commands and extracting their intent and goals.
- Task decomposition and planning: Break down complex tasks into actionable subtasks and plan them taking into account dependencies between the subtasks.
- Code generation: Generate the code required to execute the subtasks and send it to the robot control system.
- Knowledge retrieval and adaptation: Using RAG, we retrieve relevant examples from a knowledge base and adapt them to the current task.
- Multimodal information integration: Integrate information from different modalities, such as vision, force, and sound, to achieve a richer understanding of the environment.
- Interaction management: Managing interactions with users and asking questions or clarifying information as necessary.

The language processing component represents dependencies between subtasks as conditional probabilities to optimize task planning and execution. For example, P(L2A, L2B|L1) specifies the probability of proceeding to task L2A or L2B after successful execution of task L1. This allows for dynamic selection of the next subtask depending on the task progress.

2. Vision system:
The vision system is responsible for generating a 3D representation of the environment and localizing objects. The system acquires 3D information of the environment using a depth camera (e.g., Azure Kinect DK) and identifies objects based on verbal instructions through a language-vision module (e.g., Grounded-Segment-Anything).

The main functions of the visual system are:
- Generating a 3D representation of the environment: Using a depth camera, a 3D representation of the environment is generated.
- Object detection and segmentation: Detect and segment objects using the language-vision module.
- Object Pose Estimation: Estimate the position and pose of detected objects.
- Object Tracking: Track the movement of an object and update its location in real time.
- Occlusion handling: Estimate the position of an object even if it is obscured by other objects.
- Environmental change detection: Detect changes in the environment (e.g., object movement, appearance of new objects).

The vision system performs object segmentation and pose estimation to provide the information the robot needs for grasping and manipulation, and also detects changes in the environment (e.g. object movement) in real time and adapts them to the robot's movements.

3. Haptic module:
The force module is responsible for measuring the force acting on the robot's end effector and improving the precision of object manipulation. This module uses a multi-axis force sensor (e.g., ATI force sensor) to measure the force and torque acting on the robot's end effector.

The main functions of the haptic module are:
- Force and torque measurement: A multi-axis force sensor is used to measure the forces and torques experienced by the robot's end effector.
- Force transformation and interpretation: Transform the measured forces into the robot's base coordinate system and interpret their meaning.
- Force control: Control the application of appropriate forces when interacting with objects.
- Contact detection: Detects contact with an object and determines its state.
- Object property estimation: Estimate object properties such as weight, hardness, and friction from force feedback.
- Anomaly detection: Detect unexpected force changes and take safety measures.

The haptic module uses force feedback to improve the precision of object manipulation (e.g., pouring liquid, opening and closing drawers) and to continue manipulation even in situations where visual information is limited (e.g., when visibility is obstructed).

4. Speech recognition and synthesis module:
The speech recognition and synthesis module is responsible for recognizing the user's voice commands and outputting the system's responses in voice. This module consists of a speech recognition engine and a speech synthesis engine.

The main functions of the speech recognition and synthesis module are:
- Speech recognition: Recognizes the user's voice commands and converts them into text.
- Speech synthesis: Converting system responses from text to speech.
- Speaker identification: Identify the voices of multiple users and provide personalized responses.
- Emotion recognition: Recognize emotions from the user's voice and respond appropriately.
- Environmental sound recognition: Recognize environmental sounds (e.g. warning sounds, door opening and closing sounds) and understand the situation.
- Voice dialogue management: Manage voice dialogue with users to realize natural conversation.

The speech recognition and synthesis module enables natural interaction with the user, enabling hands-free operation, and by combining it with visual information, enables multi-modal dialogue.

5. Robot control system:
The robot control system is responsible for integrating feedback from language processing, vision system, force sensor module, and voice recognition and synthesis module to control the robot's movements. This system is based on ROS (Robot Operating System) and provides a control mechanism with built-in safety constraints.

The main functions of the robot control system are:
- Multimodal feedback integration: Integrating feedback from different modalities and incorporating it into the robot’s behavior.
- Motion planning and execution: Plan and execute the movements required to accomplish a task.
- Implement safety constraints: Implement safety constraints such as maximum speed and force limits, workspace boundaries, etc.
- Obstacle Avoidance: Detect obstacles in the environment and plan a path to avoid them.
- Error detection and recovery: Detect errors during operation and take appropriate recovery actions.
- Adaptive control: Adjusting control parameters to adapt to environmental changes and uncertainties.

Robot control systems incorporate safety constraints, setting maximum speed and force limits and workspace boundaries to ensure the system is safe and reliable, and can adjust their behavior based on real-time feedback.

6. Knowledge Base:
The knowledge base is a curated database of verified low- and high-level action examples, organized in a systematic way including code examples, motion primitives, and task decomposition examples.

The main components of the knowledge base are:
- Basic motion primitives: Basic movements such as linear motion, rotational motion, and gripper opening and closing.
- Object manipulation motion primitives: actions related to object manipulation such as grasping, placing, lifting, and carrying.
- Special motion primitives: Special actions like pouring liquids, scooping powder, opening and closing drawers, drawing, and more.
- Higher order task examples: Complex tasks such as making coffee, decorating a plate, or passing an object.
- Error Handling and Recovery Examples: Examples for handling and recovering from errors during operation.
- Environmental Adaptation Examples: Examples of adapting to changes and uncertainty in the environment.

The knowledge base contains motion examples that incorporate known uncertainties, allowing it to handle a wide variety of scenarios. It also has a structure that makes it easy to add and update new knowledge, ensuring the extensibility of the system.

7. Multimodal integration module:
The multimodal integration module is responsible for integrating information from different modalities such as vision, force, and sound to realize richer environmental understanding and adaptive behavior generation. This module provides a mechanism to appropriately weight and integrate information from each modality.

The main features of the Multimodal Integration module are:
- Cross-modality information integration: Integrating information from different modalities to generate a coherent representation of the environment.
- Modality Weighting: Dynamically adjust the importance of each modality depending on the task and situation.
- Cross-modal learning: Learning the relationships between different modalities to realize mutual complementarity of information.
- Multimodal reasoning: Making inferences about the environment or situation based on integrated information.
- Managing uncertainty: taking into account uncertainties in each modality and prioritizing more reliable information.
- Completion of missing information: When information from some modalities is missing, it is complemented using information from other modalities.

The multimodal integration module performs processes such as identifying the object's position from visual information, estimating the object's weight from force information, and integrating these to determine the appropriate gripping force and position, enabling more precise and adaptive object manipulation.

The operation flow of this system is as follows:

1. The user gives a high-level command (e.g., "make a coffee") via voice or text.

2. The speech recognition and synthesis module converts the spoken instructions into text and sends it to the language processing component.

3. The language processing component interprets the instructions, combines them with environmental data (images from the vision sensors) and breaks the task down into a set of subtasks (e.g., "find the mug,""scoop the coffee,""pour the water").

4. For each subtask, search the knowledge base for relevant examples using RAG, selecting the most relevant examples based on vector embedding and cosine similarity.

5. From the retrieved examples, the language model generates executable code that includes function calls to take advantage of visual and haptic feedback.

6. The generated code is sent to the robot control system and executed.

7. The robot performs the task based on the generated code, using multimodal feedback including vision, force and audio.
- Use visual feedback to locate objects and plan grasps and manipulations.
- Use force feedback to control interactions with objects (e.g. pouring liquid, opening and closing a drawer).
- Use audio feedback to continue the dialogue with the user, asking questions or providing clarification as needed.

8. The multimodal integration module integrates information from different modalities to achieve richer environmental understanding and adaptive behavior generation.

9. Incorporate real-time feedback and adjust behavior in response to changes and uncertainty in the environment.
- If the object moves, it uses visual feedback to identify its new position and adjust its behavior.
- Take evasive action if an unexpected obstacle appears.
- If the force feedback is different than expected (e.g. the drawer is heavier than expected), adjust the force.
- Update the task plan if the user gives new instructions.

10. After completing the task, report the result to the user and wait for further instructions.

The distinctive features of this system are as follows:

1. Efficient use of knowledge bases using RAG:
Our system ensures scalability by efficiently utilizing the knowledge base using RAG. Conventional approaches statically incorporate the contents of the knowledge base into the context window of the language model, which causes a problem of performance degradation as the knowledge base expands. Our system dynamically selects relevant examples from the knowledge base using RAG, so performance can be maintained even if the knowledge base expands.

2. Multimodal feedback integration:
This system realizes richer environmental understanding and adaptive behavior generation by integrating feedback from different modalities such as vision, force, and sound. Each modality provides different types of information, and by appropriately integrating them, the system can respond to complex situations that cannot be captured by a single modality.

3. Creative movement generation:
The system can generate new patterns of movement by combining language models and visual generative models. For example, a visual generative model such as DALL-E can be used to perform creative tasks such as generating silhouettes based on keywords, extracting their outlines, and drawing them on a physical surface.

4. Incorporating safety constraints:
The system ensures safety and reliability by incorporating safety constraints: constraints such as maximum speed and force limits, workspace boundaries, etc. are coded into the basic motion primitives, so errors in the language model cannot override these constraints.

5. Natural user interaction:
The system achieves natural interaction with the user through the speech recognition and synthesis module. The user can give instructions in natural language, and the system can respond in natural language. The system can also continue to dialogue with the user while the task is being performed, asking questions or clarifying information as necessary.

6. Hardware-independent design:
The system is hardware-independent and can be applied to a variety of robot platforms. It also offers flexibility in choosing the RAG infrastructure, allowing the use of open source tools such as Haystack and vebra, or the OpenAI RAG process.

These features enable the system to improve its ability to perform complex tasks in unpredictable environments and solve problems such as understanding and executing high-level abstract instructions, effectively integrating visual and haptic feedback, enhancing the ability to adapt to environmental changes and uncertainties, efficiently utilizing and ensuring scalability of the knowledge base, planning and executing long-term tasks, generating creative actions, ensuring safety and reliability, integrating and processing multimodal information, and naturally interacting with humans.

The present invention provides the following advantages:

1. Understanding and executing high-level abstract instructions:
The system can understand high-level abstract instructions in natural language (e.g., "Make coffee" or "I'm tired, make a hot drink") from the user and break them down into appropriate subtasks for execution. This allows the user to execute a task simply by conveying the purpose, without giving specific action instructions. For example, in response to a complex instruction such as "I'm tired and a friend is coming over soon to eat cake, so please make a hot drink and draw a random animal on the plate," the system can break it down into a series of subtasks, such as selecting coffee as a "hot drink for a tired person," finding a mug, scooping the coffee, pouring hot water, and further drawing an animal on the plate.

This effect is achieved by the natural language understanding capabilities of the Large Scale Language Model (LLM) and the efficient use of the knowledge base through Retrieval Augmented Generation (RAG) technology. The LLM can understand the context and intent and break down abstract instructions into concrete subtasks. In addition, RAG can search for relevant examples from the knowledge base and adapt them to the current task. This allows the system to respond to various abstract instructions and break them down into appropriate subtasks for execution.

As a concrete example, when a user instructs the system to "prepare breakfast," the system can take into account environmental data (e.g., what's in the refrigerator, whether or not there are cooking utensils) and break the task down into subtasks such as "make toast,""cookeggs," and "make coffee," and execute each subtask in the appropriate order. In addition, when a user instructs the system to "tidy up the room," the system can break the task down into subtasks such as "pick up the mess on the floor,""tidy up the table," and "throw out the trash," and execute them accordingly.

The ability to understand and execute such high-level abstract instructions greatly improves the usability and practicality of a robot. Users can give instructions to a robot in natural language without the need for specialized knowledge or detailed instructions. This makes it easier for a variety of users, including the elderly, disabled, and children, to use robots.

2. Completing long-term tasks:
The system can consistently execute long-term tasks that consist of multiple subtasks. By expressing dependencies between subtasks as conditional probabilities and optimizing task planning and execution, complex tasks can be completed efficiently. For example, for the task of making coffee, a plan can be created that takes into account dependencies such as opening a drawer when a mug is missing. The system can also adjust the plan according to the progress of the task and respond to unexpected situations (e.g., when coffee beans are in short supply).

This effect is achieved through the task decomposition and planning capabilities of the language processing component and the adaptive control capabilities of the robot control system. The language processing component decomposes a task into executable subtasks and creates a plan that takes into account dependencies between the subtasks. The robot control system also adjusts its control parameters to adapt to changes and uncertainties in the environment. This allows the system to consistently execute long-term tasks and respond to unexpected situations.

As a concrete example, consider the long-term task of "prepare dinner," and the system breaks it down into many subtasks, such as "take ingredients out of the refrigerator,""washvegetables,""cutvegetables,""cook,""preparedishes," and "plating the food." Each subtask has dependencies; for example, "cut vegetables" must be performed after "wash vegetables" is completed. The system takes these dependencies into account to create a plan and execute the tasks efficiently. Also, if it is discovered that ingredients are in short supply midway, the system can adjust the plan and take action, such as using alternative ingredients or asking the user for confirmation.

The ability to complete such long-term tasks will greatly increase the practicality and usefulness of robots. Robots will be able to consistently execute complex, multi-step tasks, rather than just repeating simple movements. This will open the door to greater use of robots in a variety of application areas, including household assistance, nursing care assistance, and complex assembly work in manufacturing.

3. Integration and use of multimodal feedback:
This system realizes richer environmental understanding and adaptive behavior generation by integrating feedback from different modalities such as vision, force, and voice. It can recognize the position and shape of objects using visual feedback, control physical interactions with objects using force feedback, and continue dialogue with the user using voice feedback. This makes it possible to respond to complex situations that cannot be captured by a single modality.

This effect is achieved through the cooperation of a vision system, a haptic module, a speech recognition and synthesis module, and a multimodal integration module. The vision system performs object detection and segmentation, and pose estimation, the haptic module performs force and torque measurement and force control, and the speech recognition and synthesis module performs speech recognition and synthesis, and dialogue management. The multimodal integration module integrates this information to generate a consistent environmental representation, which enables the system to achieve richer environmental understanding and adaptive behavior generation.

As a concrete example, consider the task of pouring liquid, the system uses visual feedback to identify the position of the cup and haptic feedback to control the amount poured. If the cup moves, the system uses visual feedback to identify its new position and adjusts its actions. Even if the cup disappears from view during pouring, the system can continuously monitor the amount poured using haptic feedback and stop the pour at the right time. Furthermore, if the user gives a voice command to "pour more," the system can understand the command and pour additional liquid using voice feedback.

The ability to integrate and utilize such multimodal feedback will greatly improve a robot's ability to understand and adapt to its environment. Robots will be able to go beyond the limitations of a single sensor and integrate multiple senses to understand the environment and act adaptively, just like humans. This will enable robots to perform complex tasks in unpredictable environments, greatly expanding the range of applications for robots.

4. Adaptation to environmental changes and uncertainties:
The system can incorporate feedback in real time to improve its ability to adapt to changes and uncertainties in the environment. If an object moves, it can use visual feedback to identify its new location and adjust its movements. If an unexpected obstacle appears, it can take evasive action. Furthermore, if the haptic feedback differs from expectation (e.g. the drawer is heavier than expected), it can adjust the force applied. This allows the system to continue performing tasks even in unpredictable environments.

This effect is achieved by the vision system's ability to detect environmental changes, the force control ability of the haptic module, and the adaptive control ability of the robot control system. The vision system detects environmental changes in real time, and the haptic module appropriately controls the interaction with objects. The robot control system dynamically adjusts control parameters based on these feedbacks. This allows the system to adapt to environmental changes and uncertainties and continue to perform the task.

As a specific example, consider the task of clearing objects off a table. The system uses visual feedback to identify the location of the object, grasp it, and place it in the designated location. If the object moves during the process, the system uses visual feedback to identify its new location and adjust the motion plan. If the object is heavier than expected, the system can detect its weight using force feedback and take action such as increasing the gripping force or lifting it with both hands. Furthermore, if an obstacle appears at the placement location, the system can detect the obstacle using visual feedback and take action such as avoiding it or placing it in a different location.

Such adaptability to environmental changes and uncertainties greatly improves the usefulness of robots in the real world. Because the real world is constantly changing and full of uncertainties, robots that can adapt to environmental changes and uncertainties can perform more practical tasks. This is expected to lead to greater use of robots in various fields, including the home, medical care, and manufacturing.

5. Efficient use and scalability of knowledge base:
This system uses RAG to efficiently utilize the knowledge base and ensure the scalability of the system. In conventional approaches, the contents of the knowledge base are statically incorporated into the context window of the language model, which causes a problem of performance degradation as the knowledge base expands. In this system, RAG is used to dynamically select relevant examples from the knowledge base, so performance can be maintained even if the knowledge base expands. This makes it easy to add and update new knowledge, making it possible to continuously improve the system's capabilities. Experimental results show that using RAG improves the fidelity score of GPT-4 from 0.74 to 0.88 and the fidelity score of GPT-3.5-turbo from 0.78 to 0.86.

This effect is achieved by the RAG function of the language processing component. RAG converts the user's query and the current task state into vector embeddings, and then calculates the cosine similarity with each chunk in the knowledge base to select the most relevant chunk. The selected chunk is added to the input of the language model, which can generate more accurate and relevant responses. This allows the system to efficiently utilize the knowledge base and ensure scalability.

As a concrete example, consider adding a new task (e.g., "make pancakes") to the system. In conventional approaches, all knowledge about making pancakes must be incorporated into the context window of the language model, which, when combined with existing knowledge, may exceed the context window limit. In our system, you simply add knowledge about making pancakes to the knowledge base, and RAG searches for related knowledge as needed and provides it to the language model. This allows the system to maintain performance even as the knowledge base expands.

Such efficient use and scalability of the knowledge base greatly improves the robot's learning and adaptability. The robot can continuously add new knowledge and handle a variety of tasks. This expands the range of applications of the robot and enables it to perform more practical tasks.

6. Creative movement generation:
The system can generate new patterns of actions by combining language models and visual generative models. For example, a visual generative model such as DALL-E can be used to generate silhouettes based on keywords (e.g., "random bird" or "random plant"), extract their outlines, and draw them on a physical surface (e.g., a plate). This allows artistic actions (e.g., drawing, decorating) to be performed based on the user's instructions. This technology can also be extended to applications such as cake decoration and latte art.

This effect is achieved by the creative action generation capability of the language processing component and the image generation capability of the visual generation model. The language processing component extracts keywords from the user's instructions and inputs them into the visual generation model. The visual generation model generates an image based on the keywords and extracts a drawing trajectory from the image. The robot control system controls the robot's action along the extracted trajectory. This enables the system to generate and execute creative actions.

As a specific example, if a user instructs the system to "draw a picture of a cat on a plate," the system extracts the keyword "cat" and inputs it to DALL-E. DALL-E generates a silhouette image of a cat, and the system extracts an outline from that image. The extracted outline is converted to match the dimensions of the plate, and the robot uses a pen to draw along that outline. In addition, in response to the instruction to "write Happy Birthday on a cake and decorate it with flowers," the system can extract the keywords "Happy Birthday" and "flowers," and generate and execute a drawing that combines text and floral decorations.

This creative motion generation ability will greatly expand the expressive power and range of applications of robots. Robots will be able to perform not only simple repetitive tasks, but also artistic expressions and individual creative activities. This is expected to lead to the use of robots in new fields such as entertainment, education, and the arts.

7. Improved safety and reliability:
The system can improve the safety and reliability of the system by incorporating safety constraints. Constraints such as maximum speed and force limits, and workspace boundaries are coded into the basic motion primitives so that errors in the language model cannot override these constraints. For example, linear velocity is limited to within ±0.05 m/s, angular velocity is limited to within ±60°/s, and the end effector force is limited to 20 N. The end effector is also limited to within predefined workspace boundaries. This ensures that the robot's movements are safe and reliable.

This effect is achieved by implementing safety constraints in the robot control system. The robot control system implements safety constraints such as maximum speed and force limits, workspace boundaries, etc., and constantly monitors these constraints. If any motion that violates the constraints is detected, the system automatically stops or corrects the motion. This ensures the safety and reliability of the system.

As a specific example, when a robot grasps an object and moves it, even if the code generated by the language model erroneously instructs a high-speed movement, the safety constraints of the robot control system limit the movement speed within a safe range. Also, if the robot approaches the boundary of the workspace, the system automatically slows down or stops the movement to prevent it from crossing the boundary. Furthermore, if there is a possibility that excessive force will be applied during interaction with an object, the system limits the force to prevent damage to the object or the environment.

Such improvements in safety and reliability are essential for the practical application and widespread use of robots. Safe and reliable robots can be used without worry in environments where they coexist with humans. This is expected to lead to greater use of robots in areas closely related to humans, such as in the home, medical care, and education.

8. Natural human interaction:
Through the speech recognition and synthesis module, the system can achieve natural interaction with the user. The user can give instructions in natural language, and the system can respond in natural language. In addition, even while the task is being performed, the system can continue to dialogue with the user, asking questions or making confirmations as necessary. This allows for smoother communication between the user and the robot, improving the user experience.

This effect is achieved by the voice dialogue management capabilities of the speech recognition and synthesis module and the dialogue management capabilities of the language processing component. The speech recognition and synthesis module recognizes the user's voice instructions and outputs the system's response in voice. The language processing component manages the dialogue with the user and asks questions or confirms as necessary. This allows the system to achieve natural interaction with the user.

As a specific example, if the user instructs the system to "make coffee," the system can respond with "Ok, I'll make coffee. Do you need milk or sugar?" to confirm the user's preferences. Also, if a problem occurs during the task (e.g., there is not enough coffee beans), the system can ask, "It looks like there are not many coffee beans. Do you want to continue or make another drink?" to seek the user's instructions. Furthermore, after the task is completed, the system can respond with, "The coffee is ready. Is there anything else I can help you with?" and wait for further instructions.

This natural ability to interact with humans greatly improves the usability and acceptability of robots. Users can operate robots through natural dialogue without special training or knowledge. This makes it easier for a wide range of users, including the elderly, disabled, and children, to use robots.

9. Scalability and flexibility:
The system is hardware-independent and can be applied to a variety of robot platforms. The RAG infrastructure can be flexibly selected, allowing the use of open source tools such as Haystack and vebra, or the OpenAI RAG process. Furthermore, the contents of the knowledge base can be flexibly expanded, allowing additional knowledge to be added to handle new tasks and environments. This makes it possible to continuously expand the scope of application of the system.

This effect is achieved by the system's modular design and standardized interfaces. Each component (language processing, vision system, haptic module, etc.) operates independently and communicates through standardized interfaces. This allows the system as a whole to continue functioning even if a particular component is replaced with a different implementation. The knowledge base is also organized in a standardized format, making it easy to add new knowledge.

As a concrete example, when applying the system to a different robot platform (e.g., a collaborative robot from Universal Robots or a quadruped robot from Boston Dynamics), all you need to do is adjust the interface of the robot control system to suit the target platform, and the other components can be used as is. Also, when changing the RAG infrastructure (e.g., changing from OpenAI's RAG process to Haystack), all you need to do is adjust the RAG-related parts of the language processing component, and the other components can be used as is. Furthermore, to accommodate a new task (e.g., "fold laundry"), you just need to add relevant examples to the knowledge base, and the system will be able to execute the task.

Such scalability and flexibility will enable the system to continue to evolve and be widely used. The system can be applied to a variety of hardware platforms and can handle a variety of tasks. This will greatly expand the scope of application of the system, and is expected to lead to its use in a variety of industrial fields.

10. Energy saving and reduced environmental impact:
This system can reduce energy consumption and environmental impact through efficient task execution. For example, the power consumption of the Kinova Gen3 robot arm is approximately 36W, while the power consumption of the NVIDIA RTX 2080 GPU is approximately 225W, and the carbon dioxide emissions for 4 minutes of task execution are estimated to be approximately 7g. This shows that the system is more energy efficient than a human performing a similar task. In addition, by optimizing the task, it is possible to reduce unnecessary movements and further improve energy efficiency.

This effect is achieved through the system's efficient task planning and execution capabilities, allowing it to execute tasks in an optimal order and minimize wasteful actions, while adapting to environmental changes and uncertainties, reducing the need for retries and corrections and improving energy efficiency.

As a specific example, considering the task of putting away multiple objects, the system can move the objects in an optimal order, taking into account their location and destination. This minimizes the travel distance and time, and reduces energy consumption. Also, if the system fails to grasp an object, it can use visual and haptic feedback to adjust the grasping position and force to increase the success rate of retry attempts. This reduces the number of retries and improves energy efficiency.

Such energy conservation and reduction of environmental impact contribute to the realization of a sustainable society. Highly energy-efficient robot systems can efficiently utilize limited energy resources and minimize the burden on the environment. This is expected to promote the spread of environmentally friendly robot technology.

With these effects, the present invention can contribute to the realization of "intelligent robots" that can cooperate with humans and execute complex tasks. In particular, applications in a wide range of fields are expected, such as assistance in unpredictable environments such as at home or in medical settings, flexible production in the manufacturing industry, and customer service in the service industry.

Hereinafter, an embodiment of the present invention will be described in detail.

### System Configuration This system consists of the following hardware and software components:

1. Robot arm:
In this embodiment, we use the Kinova Gen3 robot arm with 7 degrees of freedom. This robot arm has high degrees of freedom and precise control capabilities, making it suitable for complex manipulation tasks. The seven rotary joints provide a range of motion and flexibility close to that of a human arm. Each joint is equipped with a high-precision encoder, allowing for accurate measurement of the joint angle. In addition, each joint has a built-in torque sensor that can detect external forces. This enables advanced functions such as force control and collision detection.

Key specifications of the Kinova Gen3 robotic arm include:
- Degrees of freedom: 7 (3 shoulders, 1 elbow, 3 wrists)
- Maximum reach: 902mm
- Weight capacity: 4kg
- Repeatability: ±0.1mm
- Maximum speed: 0.5m/s
- Power consumption: 36W (normal operation)
- Weight: 8.2kg
- Communication interface: Ethernet, USB

The robot arm is controlled through ROS (Robot Operating System). ROS is an open source software framework for robot development that is suitable for linking with various hardware and building complex robotic systems. In this system, we use the Kinova ROS Kortex library to establish communication with the Kinova Gen3 robot arm.

2. Gripper:
The end of the robot arm is fitted with a Robotiq 2F-140mm gripper. This gripper can grip a wide range of objects, with a maximum opening width of 140mm. The gripping force can also be adjusted, allowing it to grip a variety of objects with the right amount of force, from delicate objects (e.g. eggs, glassware) to solid objects (e.g. tools, containers). The gripper is controlled through ROS, allowing the opening and closing speed and gripping force to be specified.

Key specifications of the Robotiq 2F-140mm gripper include:
- Maximum opening width: 140mm
- Gripping force: 10-125N (adjustable)
- Closing speed: 20-150mm/s (adjustable)
- Weight: 1kg
- Power consumption: 5W (normal operation)
- Communication Interface: USB, RS-485

The gripper is controlled through the ROS action server, which provides functions such as opening and closing the gripper, adjusting the gripping force, and monitoring the state. The gripper state (open/closed position, gripping force, etc.) is updated at a frequency of 50Hz.

3. Visual sensor:
To generate a three-dimensional representation of the environment, we use an Azure Kinect DK depth camera. This camera operates at a frame rate of 30 fps with a resolution of 640 x 576 pixels and can simultaneously acquire depth information and RGB images. The depth sensor uses the Time-of-Flight method, which allows for highly accurate depth measurement. In addition, it is equipped with a wide-angle lens, ensuring a wide viewing angle (120 degrees horizontally and 120 degrees vertically). For camera calibration, we use a 14 cm AprilTag to perform alignment between the camera and the base of the robot. This allows object position detection with an accuracy of less than 10^-6.

Key specifications of the Azure Kinect DK depth camera include:
- RGB resolution: 3840x2160 (4K), 2560x1440 (2K), 1920x1080 (FHD), 1280x720 (HD)
- Depth resolution: 640×576 (30fps), 512×512 (30fps), 1024×1024 (15fps)
- Depth mode: NFOV Unbinned (close range, high resolution), NFOV 2x2 Binned (close range, low resolution), WFOV 2x2 Binned (wide range, low resolution)
- Depth Range: 0.5-5.46m (NFOV Unbinned), 0.25-2.88m (WFOV 2x2 Binned)
- Viewing angle: 120 degrees horizontally, 120 degrees vertically
- Communication interface: USB 3.0

Image data from the camera is exposed as a ROS topic and processed by the vision system, which performs object detection and segmentation using Grounded-Segment-Anything. The pose of detected objects is updated at a frequency of approximately 1/3 Hz.

4. Force sensor:
An ATI multi-axis force sensor is used to measure the forces acting on the robot's end effector. The sensor is capable of measuring six axes (three axes of force and three axes of torque) and operates at a sampling rate of 100Hz. The measurement accuracy is approximately 2% of full scale, allowing it to detect minute changes in force. The sensor is calibrated so that the sensor indicates zero in the absence of external force to compensate for the effects of gravity. This allows for accurate prediction of the external forces acting on the end effector. The calibration process involves zeroing the sensor on one axis, rotating the sensor, and then zeroing it on the next axis.

The main specifications of the ATI multi-axis force sensor are as follows:
- Measurement axis: 6 axes (Fx, Fy, Fz, Tx, Ty, Tz)
- Measurement range: Fx, Fy: ±65N, Fz: ±200N, Tx, Ty, Tz: ±5Nm
- Resolution: Fx, Fy: 0.025N, Fz: 0.05N, Tx, Ty, Tz: 0.001Nm
- Sampling rate: 100Hz
- Communication interface: USB, Ethernet

The data from the force sensor is exposed as a ROS topic and processed by the force module, which converts the measured forces into the robot's base coordinate system and interprets their meaning. The conversion is done with the following formula:

Fglobal = Tend_effector_to_robot_base × Flocal

Here, Fglobal is the force vector in the robot's base coordinate system, Tend_effector_to_robot_base is the transformation matrix from the end effector's coordinate system to the robot's base coordinate system, and Flocal is the force vector in the end effector's local coordinate system.

5. Speech recognition and synthesis devices:
To realize voice interaction with the user, a device equipped with a microphone and speaker (e.g., Amazon Echo, Google Home, or a dedicated microphone and speaker) is used. The device recognizes the user's voice command and outputs the system's response in voice. Speech recognition and synthesis are performed using cloud-based services (e.g., Amazon Alexa, Google Assistant) or locally executed speech recognition and synthesis engines (e.g., Mozilla DeepSpeech, Festival).

The main specifications of the speech recognition and synthesis device are as follows:
- Microphone: Array microphone capable of far-field voice recognition
- Speakers: High quality speakers for clear audio output
- Communication interface: Wi-Fi, Bluetooth, USB
- Supported languages: Japanese, English and other major languages
- Voice recognition accuracy: 90% or higher (general conversation)
- Speech synthesis quality: natural intonation and pronunciation

Data from the speech recognition and synthesis device is published as a ROS topic and processed by the speech recognition and synthesis module, which converts the user's voice instructions into text and sends it to the language processing component, and converts the text response from the language processing component into speech and outputs it to the user.

6. Computer:
To process the system, we use a desktop computer equipped with an Intel Core i9 processor and an NVIDIA RTX 2080 GPU. This computer is connected to the robot arm, the visual sensor, the force sensor, and the voice recognition and synthesis device via Ethernet or USB cables. The GPU is used to execute visual processing and language models, enabling high-speed processing. In addition, the system is equipped with sufficient memory (RAM 32GB or more) to process large amounts of data and perform complex calculations.

The main specifications of the computer are as follows:
- Processor: Intel Core i9-9900K (8 cores, 16 threads, 3.6GHz)
- GPU: NVIDIA RTX 2080 (8GB VRAM)
- Memory: 32GB DDR4 RAM
- Storage: 1TB NVMe SSD
- OS: Ubuntu 20.04 LTS
- Network: Gigabit Ethernet, Wi-Fi 6

The computer runs software such as ROS, vision processing libraries (OpenCV, PCL), machine learning frameworks (PyTorch, TensorFlow), language model APIs (OpenAI API), etc. These software are used to implement each component of the system (language processing, vision system, haptic module, etc.).

7. Operating System:
The system uses Ubuntu 20.04 as its software platform. This operating system is highly compatible with ROS and provides stable operation. It also supports many robot-related libraries and tools, making it suitable as a development environment.

Key features of Ubuntu 20.04 include:
- Kernel: Linux 5.4
- Desktop Environment: GNOME 3.36
- Package Manager: APT
- Support period: 5 years (until April 2025)
- Security: Regular security updates
- Hardware Compatibility: Supports a wide range of hardware

Ubuntu is an officially supported OS for ROS, making it easy to install and configure ROS. In addition, many robot-related libraries and tools are packaged for Ubuntu, making it easy to build a development environment.

8. Robot control software:
The robot is controlled using ROS (Robot Operating System). ROS is an open source software framework for robot development, and is suitable for linking with various hardware and building complex robot systems. In this system, the Kinova ROS Kortex library is used to establish communication with the Kinova Gen3 robot arm. Using the inter-node communication function of ROS, feedback from the language processing, vision system, and force sensor module is integrated to control the robot's movements.

The main functions of ROS are:
- Inter-node communication: Communication between nodes through topics, services, and actions
- Distributed computing: Building robotic systems across multiple computers
- Hardware abstraction: A unified interface for handling various hardware
- Debugging tools: Tools to help debug robot systems (RViz, rqt)
- Simulation: Linkage with simulators such as Gazebo
- Package management: Managing robot-related software packages

ROS provides three communication methods: topics, services, and actions. Topics are used for asynchronous one-way communication and are suitable for things like distributing sensor data. Services are used for synchronous request-response communication and are suitable for things like calculations and queries. Actions are used to control long-running tasks and are suitable for things like controlling the movements of robots. In this system, these communication methods are appropriately combined to realize communication between each component.

9. Language model:
To process user queries and environmental data, we use large-scale language models such as GPT-4. GPT-4 has high natural language understanding and generation capabilities, and can understand complex instructions and generate appropriate code. In this system, language processing is performed through the GPT-4 API. To implement RAG, we use OpenAI's RAG process or open source tools such as Haystack and vebra.

Key features of GPT-4 include:
- Number of parameters: Not disclosed (GPT-3 has 175 billion)
- Context window: 8192 tokens (approximately 32,000 words)
- Language comprehension ability: Understand complex instructions and contexts and respond appropriately
- Code generation capabilities: Generate code for various programming languages
- Reasoning skills: performing logical reasoning, common sense reasoning, mathematical reasoning, etc.
- Multilingual: Supports many languages including English, Japanese, Chinese, Spanish, etc.

The GPT-4 API allows you to set the following parameters:
- Model: gpt-4, gpt-4-0613 etc.
- Max tokens: The maximum length of the text to generate.
- Temperature: Controls the diversity of generation (0-2, lower is more decisive)
- Top P: controls the variation of generation (0-1, lower is more deterministic)
- Frequency penalty: suppress repetition (0-2, higher is more suppressive)
- Presence penalty: encourages the introduction of new topics (0-2, higher is more encouraging)

The system uses the following settings:
- Model: gpt-4-0613
- Maximum number of tokens: 8192
- Temperature: 0.7 (to balance creativity and consistency)
- Top P: 0.95 (to generate diverse responses)
- Frequency penalty: 0.0 (to suppress repetition)
- Presence penalty: 0.0 (to encourage the introduction of new topics)

10. Visual Processing Module:
For object identification and segmentation, we use a language-vision model such as Grounded-Segment-Anything, which can identify objects and generate segmentation masks based on linguistic instructions. We also use MobileSAM to create segmented masks and 3D voxels that encompass detected objects, allowing us to extract the object pose, which can be used for robotic grasping and manipulation.

The main features of Grounded-Segment-Anything are:
- Language-Vision Model: Identify objects based on verbal instructions
- Segmentation: Generate detailed segmentation masks of objects
- Zero-shot transfer: Objects not in the training data can be identified
- Real-time processing: High-speed processing possible (approx. 0.3 seconds/frame)
- High accuracy: AP (average precision) of 52.5 on COCO zero-shot forward benchmark

The visual processing module performs the following operations:
- Image pre-processing: noise removal, contrast adjustment, etc.
- Object detection: Detect objects based on language instructions
- Segmentation: Generate segmentation masks for detected objects
- 3D reconstruction: combines depth information with segmentation masks to create a 3D model of the object
- Pose Estimation: Estimate the position and orientation of an object
- Tracking: Track the movement of an object

The vision processing module is implemented as a ROS node, processes image data from the camera, and publishes the pose of detected objects as a topic, allowing the robot control system to understand the object's position and orientation and perform appropriate grasping and manipulation.

11. Knowledge Base:
The system's knowledge base uses a curated database containing examples of verified low- and high-level actions. The database systematically organizes code examples, motion primitives, task decomposition examples, and more. It also includes motion examples that incorporate known uncertainties to address a wide variety of scenarios. The knowledge base format is chosen to be compatible with the RAG system, such as a Markdown file or structured JSON.

The main components of the knowledge base are:
- Basic motion primitives: Basic movements such as linear motion, rotational motion, and gripper opening and closing
- Object manipulation motion primitives: actions related to object manipulation such as grasping, placing, lifting, and carrying
- Special motion primitives: special actions like pouring liquids, scooping powder, opening and closing drawers, drawing, etc.
- High-level task examples: complex tasks such as making coffee, decorating a plate, or passing an object
- Error handling and recovery examples: Examples for handling and recovering from errors during operation
- Examples of environmental adaptation: Examples of adapting to changes and uncertainty in the environment

Each example includes the following information:
- Code: Runnable Python code
- Input parameters: parameters required to run the code (e.g. target position, velocity, force)
- Output: The result of the code execution (e.g. success/failure, error message)
- Preconditions: conditions that are necessary for the code to run (e.g. a certain object must exist, or the robot must be in a certain initial state)
- Subsequent conditions: the expected state after the code is executed (e.g. an object is placed in a certain position, a robot is in a certain state)
- Uncertainty: Uncertainty associated with the execution of the code (e.g. uncertainty in the position of an object, precision of the control of forces)
- Coping methods: ways to deal with uncertainty (e.g., using visual feedback, using haptic feedback)

The knowledge base is searched by the RAG system to provide relevant examples to the language model, which can then generate responses appropriate to the task at hand.

### Linguistic Processing Component The linguistic processing component is responsible for processing the user query and environmental data, and breaking down complex tasks into a series of actionable steps.

#### Language model selection and configuration In this embodiment, GPT-4 is used as the language model. GPT-4 has high natural language understanding and generation capabilities, and can understand complex instructions and generate appropriate code. Language processing is performed through the GPT-4 API. The configuration parameters of the API are as follows:

- Model: gpt-4-0613
- Maximum number of tokens: 8192
- Temperature: 0.7 (to balance creativity and consistency)
- Top P: 0.95 (to generate diverse responses)
- Frequency penalty: 0.0 (to suppress repetition)
- Presence penalty: 0.0 (to encourage the introduction of new topics)

These parameters can be adjusted depending on the nature of the task and the level of creativity required: for example, if a more decisive response is required, Temperature and Top P can be set lower, and if a more creative response is required, these parameters can be set higher.

The input (prompt) for GPT-4 consists of the following:
- System messages: Specify model roles and behavior constraints
- User queries: User instructions and questions
- Environmental data: Images from visual sensors and environmental conditions
- Related examples from the knowledge base: Related examples retrieved by RAG

Example of a system message:
```
You are an assistant in a robot control system. Based on user instructions and environmental data, analyze the tasks the robot should perform and generate the appropriate Python code. The code runs in a ROS environment and makes use of visual and force feedback. Prioritize safety and respect constraints such as maximum speed and force limits, workspace boundaries, etc.
```

Such prompt design allows GPT-4 to understand appropriate roles and generate safe and effective code.

#### Task Decomposition Process The language processing component takes the user's high-level instructions (e.g., "make a coffee") and, combined with the environment data (images from the vision sensor), decomposes the task into a set of subtasks. This process involves the following steps:

1. Parse the user’s instructions and identify the primary goal (e.g., “make coffee”).
2. Analyze environmental data and identify available resources (e.g., mugs, coffee beans, kettles).
3. Identify the subtasks required to achieve the goal. For example, the task "make coffee" can be broken down into the following subtasks:
- Find a mug
- Find coffee beans
- Find the kettle
- Place the mug in the right position
- Scooping coffee beans
- Put coffee beans into a mug
- Pour hot water from the kettle
4. Identify dependencies between subtasks. For example, "put the coffee beans in the mug" depends on "put the mug in its proper position."
5. Define execution conditions and success criteria for each subtask. For example, the success criterion for the subtask "find a mug" is that the mug is located and the robot is ready to grasp it.

This task decomposition process is expressed in the form of conditional probabilities, e.g., P(L2A, L2B|L1) specifies the probability of proceeding to task L2A or L2B after successful execution of task L1, allowing the next subtask to be dynamically selected depending on the task progress.

Specifically, the following conditional probabilities are defined:
- P(finding a mug|initial state) = 0.9: The probability of finding a mug in the initial state is 90%
- P(open drawer|can't find mug) = 0.8: If you can't find the mug, there is an 80% chance that you will open the drawer
- P(find mug|open drawer) = 0.7: After opening the drawer, there is a 70% chance of finding the mug.

These conditional probabilities are based on knowledge-based examples and past experience, and can be dynamically updated based on feedback obtained while performing the task.

#### Retrieval Augmented Generation (RAG) Implementation Our system uses RAG to search for relevant examples from the knowledge base and improve the output of the language model. To implement RAG, we take the following steps:

1. Query Embedding: Convert the user query and the current task state into a vector embedding. We use embedding models such as OpenAI's text-embedding-ada-002 and HuggingFace's Sentence-BERT.
2. Chunking: Divide the knowledge base into meaningful units (chunks), the size of which is determined by balancing the need to retain context and the efficiency of retrieval.
3. Chunk embedding: Convert each chunk into a vector embedding.
4. Similarity computation: Compute the cosine similarity between the query embedding and each chunk embedding.
5. Top Chunk Selection: Select the top k chunks based on their similarity, where the value of k is tuned depending on the complexity of the task and the size of the knowledge base.
6. Context expansion: Add the selected chunk to the input to the language model.
7. Response generation: Using the extended context, the language model generates a response (code).

In this embodiment, we use OpenAI's RAG process to organize the curated knowledge base as a markdown file. However, other RAG approaches using tools such as Haystack and vebra are also available within the framework of our system. When using these tools, the following components can be selected:

- Document store: How to store and organize your knowledge base (e.g. Markdown files, Elasticsearch)
- Embedders: Models that convert text into vector representations (e.g. OpenAI Embeddings, Sentence-BERT)
- Retrievers: How to find documents related to a query (e.g. vector search, keyword search, hybrid search)
- Chunking techniques: ways to split a document into meaningful units (e.g. fixed size, paragraph-based, semantic chunking)
- Language model: the model that generates the final response (e.g. GPT-4, GPT-3.5-turbo, Zephyr-7B-beta)

An example of a RAG implementation could be the following Python code:

```python
from openai import OpenAI
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# Initialize the OpenAI API client
client = OpenAI(api_key="your-api-key")

# Dictionary that stores the knowledge base chunks and their embeddings
knowledge_base = {}

# Initialize the knowledge base (extract chunks from markdown files and calculate embeddings)
def initialize_knowledge_base(markdown_file):
with open(markdown_file, 'r') as f:
content = f.read()

# Splitting Markdown into meaningful chunks
chunks = split_into_chunks(content)

# Calculate the embedding of each chunk
for chunk in chunks:
embedding = get_embedding(chunk)
knowledge_base[chunk] = embedding

# Get text embeddings
def get_embedding(text):
response = client.embeddings.create(
model="text-embedding-ada-002",
input=text
)
return response.data[0].embedding

# Find chunks related to the query
def retrieve_relevant_chunks(query, k=5):
# Get the query embedding
query_embedding = get_embedding(query)

# Calculate the similarity between each chunk and the query
similarities = {}
for chunk, chunk_embedding in knowledge_base.items():
similarity = cosine_similarity([query_embedding], [chunk_embedding])[0][0]
similarities[chunk] = similarity

# Select the top k chunks based on similarity
sorted_chunks = sorted(similarities.items(), key=lambda x: x[1], reverse=True)
top_k_chunks = [chunk for chunk, _ in sorted_chunks[:k]]

return top_k_chunks

# Response generation using RAG
def generate_response_with_rag(query, system_message, k=5):
# Search for related chunks
relevant_chunks = retrieve_relevant_chunks(query, k)

# Extend the context
context = "\n\n".join(relevant_chunks)

# Send input to the language model
response = client.chat.completions.create(
model="gpt-4-0613",
messages=[
{"role": "system", "content": system_message},
{"role": "user", "content": f"Query: {query}\n\nRelevant Information:\n{context}"}
],
max_tokens=8192,
temperature=0.7,
top_p=0.95,
frequency_penalty=0.0,
presence_penalty=0.0
)

return response.choices[0].message.content
```

Such an implementation of RAG allows the system to efficiently search for relevant examples from the knowledge base and improve the output of the language model.

#### Code Generation and Execution The language processing component generates executable code based on the relevant examples obtained through RAG. The generated code is written in ROS-compatible Python (v.3.8) and contains the following elements:

1. Importing required libraries and modules
2. Initialize the ROS node
3. Function calls to obtain visual and haptic feedback
4. Main function for executing the task
5. Error Handling and Recovery Mechanisms
6. Implementing Safety Constraints

Example of generated code:

```python
#!/usr/bin/env python3

# Importing required libraries and modules
import rospy
import numpy as np
from std_msgs.msg import String
from geometry_msgs.msg import Pose, Twist
from sensor_msgs.msg import Image, JointState
from kortex_driver.srv import *
from kortex_driver.msg import *
from cv_bridge import CvBridge
import tf2_ros
import tf2_geometry_msgs
from tf.transformations import quaternion_from_euler

# Initialize the ROS node
rospy.init_node('coffee_making_node', anonymous=True)

# Global variables
robot_name = "my_gen3"
bridge = CvBridge()
tf_buffer = tf2_ros.Buffer()
tf_listener = tf2_ros.TransformListener(tf_buffer)

# Service client configuration
base_service_name = '/' + robot_name + '/base/base_service'
base_client = rospy.ServiceProxy(base_service_name, Base_ClearFaults)

# Function to get visual feedback
def get_object_pose(object_name):
try:
# Get object detection results
object_pose = rospy.wait_for_message('/vision/object_poses', Pose, timeout=5.0)
return object_pose
except rospy.ROSException:
rospy.logerr(f"Failed to get pose for object: {object_name}")
return None

# Function to get force feedback
def get_force_feedback():
try:
# Get the force sensor reading
force_data = rospy.wait_for_message('/force_torque_sensor/wrench', WrenchStamped, timeout=5.0)
return force_data.wrench
except rospy.ROSException:
rospy.logerr("Failed to get force feedback")
return None

# Gripper control function
def control_gripper(position, speed=0.1, force=0.8):
try:
# Call the gripper control service
service_name = '/' + robot_name + '/base/send_gripper_command'
gripper_client = rospy.ServiceProxy(service_name, SendGripperCommand)

# Setting gripper commands
gripper_command = SendGripperCommandRequest()
gripper_command.input.gripper.finger[0].value = position
gripper_command.input.mode = GripperMode.GRIPPER_POSITION

# Send gripper commands
gripper_client(gripper_command)

return True
except rospy.ServiceException as e:
rospy.logerr(f"Failed to control gripper: {e}")
return False

# Function to move the robot
def move_robot(target_pose, speed=0.1):
try:
# Set the cart esian speed command publisher
velocity_pub = rospy.Publisher('/' + robot_name + '/in/cartesian_velocity', TwistCommand, queue_size=10)

# Get current position
current_pose = rospy.wait_for_message('/' + robot_name + '/base/tool_pose', Pose, timeout=5.0)

# Calculate the direction vector to the target position
direction = np.array([
target_pose.position.x - current_pose.position.x,
target_pose.position.y - current_pose.position.y,
target_pose.position.z - current_pose.position.z
])

# Normalize the direction vector
distance = np.linalg.norm(direction)
if distance > 0:
direction = direction / distance

# Set speed command
twist_cmd = TwistCommand()
twist_cmd.reference_frame = CartesianReferenceFrame.CARTESIAN_REFERENCE_FRAME_BASE
twist_cmd.twist.linear_x = direction[0] * speed
twist_cmd.twist.linear_y = direction[1] * speed
twist_cmd.twist.linear_z = direction[2] * speed

# Send speed commands until the target position is reached
rate = rospy.Rate(10) # 10Hz
while distance > 0.01:
# Get current position
current_pose = rospy.wait_for_message('/' + robot_name + '/base/tool_pose', Pose, timeout=5.0)

# Recalculate the direction vector to the target position
direction = np.array([
target_pose.position.x - current_pose.position.x,
target_pose.position.y - current_pose.position.y,
target_pose.position.z - current_pose.position.z
])

# Normalize the direction vector
distance = np.linalg.norm(direction)
if distance > 0:
direction = direction / distance

# Update speed command
twist_cmd.twist.linear_x = direction[0] * min(speed, distance)
twist_cmd.twist.linear_y = direction[1] * min(speed, distance)
twist_cmd.twist.linear_z = direction[2] * min(speed, distance)

# Send speed command
velocity_pub.publish(twist_cmd)

rate.sleep()

# Send stop command
twist_cmd.twist.linear_x = 0.0
twist_cmd.twist.linear_y = 0.0
twist_cmd.twist.linear_z = 0.0
velocity_pub.publish(twist_cmd)

return True
except rospy.ROSException as e:
rospy.logerr(f"Failed to move robot: {e}")
return False

# Function to find mugs
def find_mug():
rospy.loginfo("Finding mug...")

# Get the position of the mug
mug_pose = get_object_pose("mug")

if mug_pose is None:
rospy.logwarn("Mug not found in the current view. Searching in drawers...")
# Call the process to open the drawer
open_drawer()
# Get the mug position again
mug_pose = get_object_pose("mug")

if mug_pose is None:
rospy.logerr("Failed to find mug even after opening drawer.")
return None

rospy.loginfo(f"Mug found at position: {mug_pose.position.x}, {mug_pose.position.y}, {mug_pose.position.z}")
return mug_pose

# Function to open the drawer
def open_drawer():
rospy.loginfo("Opening drawer...")

# Get the drawer handle position
handle_pose = get_object_pose("drawer_handle")

if handle_pose is None:
rospy.logerr("Failed to find drawer handle.")
return False

# Approach the handle
approach_pose = Pose()
approach_pose.position.x = handle_pose.position.x - 0.1
approach_pose.position.y = handle_pose.position.y
approach_pose.position.z = handle_pose.position.z
approach_pose.orientation = handle_pose.orientation

move_robot(approach_pose)

# Grab the handle
move_robot(handle_pose)
control_gripper(0.5) # Close the gripper

# Get force feedback
initial_force = get_force_feedback()

if initial_force is None:
rospy.logerr("Failed to get force feedback.")
control_gripper(1.0) # Open the gripper
return False

# Pull the drawer
pull_pose = Pose()
pull_pose.position.x = handle_pose.position.x - 0.3
pull_pose.position.y = handle_pose.position.y
pull_pose.position.z = handle_pose.position.z
pull_pose.orientation = handle_pose.orientation

# Pull while monitoring force feedback
try:
velocity_pub = rospy.Publisher('/' + robot_name + '/in/cartesian_velocity', TwistCommand, queue_size=10)

twist_cmd = TwistCommand()
twist_cmd.reference_frame = CartesianReferenceFrame.CARTESIAN_REFERENCE_FRAME_BASE
twist_cmd.twist.linear_x = -0.05 # Pull slowly

rate = rospy.Rate(10) # 10Hz

# Pull until the drawer opens or until the force exceeds a threshold
max_force = 10.0 # Maximum force (N)
max_duration = rospy.Duration(5.0) # Maximum subtraction time
start_time = rospy.Time.now()

while (rospy.Time.now() - start_time) < max_duration:
# Get force feedback
force = get_force_feedback()

if force is None:
break

# If the force exceeds a threshold, stop pulling
if abs(force.force.x) > max_force:
rospy.logwarn("Force threshold exceeded. Stopping pull.")
break

# Send speed command
velocity_pub.publish(twist_cmd)

rate.sleep()

# Send stop command
twist_cmd.twist.linear_x = 0.0
velocity_pub.publish(twist_cmd)

except rospy.ROSException as e:
rospy.logerr(f"Failed to pull drawer: {e}")
control_gripper(1.0) # Open the gripper
return False

# Open the gripper
control_gripper(1.0)

rospy.loginfo("Drawer opened successfully.")
return True

# Function to scoop coffee
def scoop_coffee():
rospy.loginfo("Scooping coffee...")

# Get the location of the coffee container
coffee_container_pose = get_object_pose("coffee_container")

if coffee_container_pose is None:
rospy.logerr("Failed to find coffee container.")
return False

# Get spoon position
spoon_pose = get_object_pose("spoon")

if spoon_pose is None:
rospy.logerr("Failed to find spoon.")
return False

# Holding a spoon
move_robot(spoon_pose)
control_gripper(0.5) # Close the gripper

# Lift the spoon
lift_pose = Pose()
lift_pose.position.x = spoon_pose.position.x
lift_pose.position.y = spoon_pose.position.y
lift_pose.position.z = spoon_pose.position.z + 0.1
lift_pose.orientation = spoon_pose.orientation

move_robot(lift_pose)

# Move it over the coffee container
above_container_pose = Pose()
above_container_pose.position.x = coffee_container_pose.position.x
above_container_pose.position.y = coffee_container_pose.position.y
above_container_pose.position.z = coffee_container_pose.position.z + 0.1
above_container_pose.orientation = spoon_pose.orientation

move_robot(above_container_pose)

# Insert spoon into coffee container
scoop_pose = Pose()
scoop_pose.position.x = coffee_container_pose.position.x
scoop_pose.position.y = coffee_container_pose.position.y
scoop_pose.position.z = coffee_container_pose.position.z + 0.02
scoop_pose.orientation = spoon_pose.orientation

move_robot(scoop_pose)

# Move the spoon horizontally to scoop up the coffee
scoop_motion_pose = Pose()
scoop_motion_pose.position.x = coffee_container_pose.position.x + 0.05
scoop_motion_pose.position.y = coffee_container_pose.position.y
scoop_motion_pose.position.z = coffee_container_pose.position.z + 0.02
scoop_motion_pose.orientation = spoon_pose.orientation

move_robot(scoop_motion_pose)

# Lift the spoon
lift_after_scoop_pose = Pose()
lift_after_scoop_pose.position.x = scoop_motion_pose.position.x
lift_after_scoop_pose.position.y = scoop_motion_pose.position.y
lift_after_scoop_pose.position.z = scoop_motion_pose.position.z + 0.1
lift_after_scoop_pose.orientation = spoon_pose.orientation

move_robot(lift_after_scoop_pose)

rospy.loginfo("Coffee scooped successfully.")
return True

# Function to pour hot water
def pour_water(mug_pose):
rospy.loginfo("Pouring water...")

# Get the kettle position
kettle_pose = get_object_pose("kettle")

if kettle_pose is None:
rospy.logerr("Failed to find kettle.")
return False

# Grab the kettle
move_robot(kettle_pose)
control_gripper(0.5) # Close the gripper

# Lift the kettle
lift_pose = Pose()
lift_pose.position.x = kettle_pose.position.x
lift_pose.position.y = kettle_pose.position.y
lift_pose.position.z = kettle_pose.position.z + 0.1
lift_pose.orientation = kettle_pose.orientation

move_robot(lift_pose)

# Move over the mug
above_mug_pose = Pose()
above_mug_pose.position.x = mug_pose.position.x
above_mug_pose.position.y = mug_pose.position.y
above_mug_pose.position.z = mug_pose.position.z + 0.2
above_mug_pose.orientation = kettle_pose.orientation

move_robot(above_mug_pose)

# Tilt the kettle
pour_orientation = quaternion_from_euler(0, 0.5, 0) # Tilt about 30 degrees
pour_pose = Pose()
pour_pose.position = above_mug_pose.position
pour_pose.orientation.x = pour_orientation[0]
pour_pose.orientation.y = pour_orientation[1]
pour_pose.orientation.z = pour_orientation[2]
pour_pose.orientation.w = pour_orientation[3]

# Pouring while monitoring force feedback
try:
# Get the initial force
initial_force = get_force_feedback()

if initial_force is None:
rospy.logerr("Failed to get initial force feedback.")
return False

# Tilt the kettle
move_robot(pour_pose)

# Wait for a certain period of time (hot water is poured)
rospy.sleep(3.0)

# Get current power
current_force = get_force_feedback()

if current_force is None:
rospy.logerr("Failed to get current force feedback.")
return False

# Estimate the amount poured from the change in force
force_change = current_force.force.z - initial_force.force.z
poured_amount = force_change / 9.8 # Convert force change to mass (approximate)

rospy.loginfo(f"Estimated poured amount: {poured_amount} kg")

# Put the kettle back in its original position
move_robot(above_mug_pose)

except rospy.ROSException as e:
rospy.logerr(f"Failed to pour water: {e}")
return False

# Put the kettle back in place
move_robot(lift_pose)
move_robot(kettle_pose)

# Open the gripper
control_gripper(1.0)

rospy.loginfo("Water poured successfully.")
return True

# Main function
def make_coffee():
rospy.loginfo("Starting coffee making process...")

try:
# Find a mug
mug_pose = find_mug()

if mug_pose is None:
rospy.logerr("Failed to find mug. Aborting coffee making.")
return False

# Scooping coffee
if not scoop_coffee():
rospy.logerr("Failed to scoop coffee. Aborting coffee making.")
return False

# Pour coffee into a mug
# (This part is omitted, but it is necessary to move the spoon over the mug and tilt it to pour the coffee.)

# Pour hot water
if not pour_water(mug_pose):
rospy.logerr("Failed to pour water. Aborting coffee making.")
return False

rospy.loginfo("Coffee made successfully!")
return True

except Exception as e:
rospy.logerr(f"An error occurred during coffee making: {e}")
return False

# Main execution
if __name__ == '__main__':
try:
# Initialize the robot
rospy.wait_for_service(base_service_name, timeout=10)
base_client()

# Make coffee
make_coffee()

except rospy.ROSInterruptException:
pass
```

Such code is generated from relevant examples obtained through RAG and written in a ROS-compatible format. The code includes function calls to utilize visual and haptic feedback, allowing it to respond to environmental changes and uncertainties. It also incorporates safety constraints (speed limits, force limits, etc.).

The generated code is executed in a secure environment that restricts access to only predefined functions to ensure the safety of the system, and performs syntax and security checks to detect potential problems before the code is executed.

### Visual System The visual system is responsible for generating a three-dimensional representation of the environment and locating objects.

#### Camera Setup and Calibration In this embodiment, we use an Azure Kinect DK depth camera. The camera settings are as follows:

- Resolution: 640 x 576 pixels
- Frame rate: 30fps
- Depth mode: NFOV Unbinned (close range, high resolution)
- Color format: BGRA
- Exposure time: automatic adjustment

To calibrate the camera, we use a 14cm AprilTag, a marker with known size and shape that is used for alignment between the camera and the robot's base. The calibration process involves the following steps:

1. Place the AprilTag at a known location in the robot's workspace.
2. The camera captures the AprilTag and detects its position and size.
3. Calculate the camera's intrinsic parameters (focal length, principal point) and extrinsic parameters (position, orientation) from the detected position and size.
4. Using the calculated parameters, find the transformation matrix from the camera coordinate system to the robot's base coordinate system.

This calibration allows the object to be located with an accuracy of less than 10^-6. The transformation matrix is given by:

PR = TAR × (TCA × PC)

Here, PC is a point in the camera coordinate system, TCA is a transformation matrix from the camera coordinate system to the AprilTag coordinate system, TAR is a transformation matrix from the AprilTag coordinate system to the robot's base coordinate system, and PR is a point in the robot's base coordinate system.

The calibration process is implemented using the ROS tf2 library, which is a library for managing transformations between different coordinate systems, including time-varying transformations.

#### Object Detection and Segmentation For object detection and segmentation, we use Grounded-Segment-Anything. This model can identify objects and generate segmentation masks based on language instructions. The process flow is as follows:

1. Input the image obtained from the camera into the Grounded-Segment-Anything model.
2. The model detects the corresponding object based on the linguistic instruction (e.g., "white mug" or "black kettle").
3. Generate bounding boxes for detected objects.
4. Use MobileSAM to create a segmented mask.
5. Combine with the depth information to create 3D voxels that encompass the detected objects.
6. Extract the object pose (position and orientation) from the voxels.

Through this process, the 3D position and orientation of the object can be determined, which can then be used by the robot to grasp and manipulate it. Object detection accuracy varies depending on the type of object and environmental conditions, but experimental results show that the detection accuracy of a white mug is about 70%, that of a black kettle is about 79%, and that of a hand is about 53%.

The implementation of Grounded-Segment-Anything is done with the following Python code:

```python
import torch
import numpy as np
import cv2
from PIL import Image
from groundingdino.util.inference import load_model, load_image, predict
from segment_anything import sam_model_registry, SamPredictor
import rospy
from sensor_msgs.msg import Image as RosImage
from geometry_msgs.msg import Pose
from cv_bridge import CvBridge

class ObjectDetector:
def __init__(self):
# Loading the Grounding DINO model
self.grounding_dino_model = load_model("groundingdino/config/GroundingDINO_SwinT_OGC.py", "groundingdino/weights/groundingdino_swint_ogc.pth")

# Loading the SAM model
self.sam = sam_model_registry["vit_h"](checkpoint="sam_vit_h_4b8939.pth")
self.sam_predictor = SamPredictor(self.sam)

# ROS related settings
self.bridge = CvBridge()
self.image_sub = rospy.Subscriber("/camera/color/image_raw", RosImage, self.image_callback)
self.depth_sub = rospy.Subscriber("/camera/depth/image_raw", RosImage, self.depth_callback)
self.pose_pub = rospy.Publisher("/vision/object_poses", Pose, queue_size=10)

# Variables to store the latest image and depth information
self.latest_image = None
self.latest_depth = None

# Classes to detect
self.classes = ["mug", "kettle", "spoon", "coffee_container", "drawer_handle"]

def image_callback(self, msg):
# Convert ROS Image type to OpenCV image
self.latest_image = self.bridge.imgmsg_to_cv2(msg, "bgr8")

def depth_callback(self, msg):
# Convert ROS Image type to OpenCV depth image
self.latest_depth = self.bridge.imgmsg_to_cv2(msg, "32FC1")

def detect_objects(self, text_prompt):
if self.latest_image is None or self.latest_depth is None:
rospy.logwarn("No image or depth data available.")
return None

# Image preprocessing
image_pil = Image.fromarray(self.latest_image)
image_tensor, _ = load_image(image_pil)

# Object detection with Grounding DINO
boxes, logits, phrases = predict(
model=self.grounding_dino_model,
image=image_tensor,
caption=text_prompt,
box_threshold=0.3,
text_threshold=0.25
)

# If there are no detection results
if len(boxes) == 0:
rospy.logwarn(f"No objects detected for prompt: {text_prompt}")
return None

# Segmentation with SAM
self.sam_predictor.set_image(self.latest_image)

result_poses = []

for box, logit, phrase in zip(boxes, logits, phrases):
# Get the bounding box coordinates
x0, y0, x1, y1 = box

# Segmentation with SAM
sam_mask, _, _ = self.sam_predictor.predict(
box=np.array([x0, y0, x1, y1]),
multimask_output=False
)

# Calculate 3D position from depth information using mask
mask = sam_mask[0].astype(np.uint8)
masked_depth = self.latest_depth.copy()
masked_depth[mask == 0] = 0

# Extract pixels with valid depth values
valid_depth = masked_depth[masked_depth > 0]

if len(valid_depth) == 0:
rospy.logwarn(f"No valid depth data for object: {phrase}")
continue

# Calculate the median depth (noise resistant)
median_depth = np.median(valid_depth)

# Calculate the bounding box center
center_x = (x0 + x1) / 2
center_y = (y0 + y1) / 2

# Transformation from image coordinate system to 3D coordinate system
# (camera internal parameters required)
fx = 500 # Camera focal length x (replace with actual value)
fy = 500 # Camera focal length y (replace with actual value)
cx = 320 # Principal point x (replace with actual value)

# Calculate 3D coordinates
z = median_depth
x = (center_x - cx) * z / fx
y = (center_y - cy) * z / fy

# Create a Pose message
pose = Pose()
pose.position.x = x
pose.position.y = y
pose.position.z = z

# For simplicity, the orientation is a unit quaternion
pose.orientation.x = 0.0
pose.orientation.y = 0.0
pose.orientation.z = 0.0
pose.orientation.w = 1.0

# Add results
result_poses.append((phrase, pose, logit.item()))

# Sort by confidence
result_poses.sort(key=lambda x: x[2], reverse=True)

# Publish the results
for phrase, pose, _ in result_poses:
rospy.loginfo(f"Detected {phrase} at position: {pose.position.x}, {pose.position.y}, {pose.position.z}")
self.pose_pub.publish(pose)

return result_poses

def detect_specific_object(self, object_name):
# Create a prompt to detect a specific object
text_prompt = object_name

# Run object detection
results = self.detect_objects(text_prompt)

if results is None or len(results) == 0:
return None

# Return the most reliable result
return results[0][1] # Return Pose
```

This code is implemented as a ROS node and processes images and depth information from the camera to detect object poses and publish them as ROS topics, which are used by the robot control system to grasp and manipulate objects.

#### Handling Occlusion and Uncertainty The vision system has mechanisms to deal with occlusion (when parts of an object are hidden by other objects) and uncertainty. To handle occlusion, the following approaches are used:

1. Partial visibility based object detection: Detect an object based on its features even if only part of it is visible.
2. Temporal tracking: Even if an object is temporarily hidden, it continues to be tracked based on its past location information.
3. Multiple Viewpoints: When possible, combine observations from different angles to reduce the effects of occlusion.

The following approaches are taken to deal with uncertainty:

1. Probabilistic representation: Represent the object position and orientation as a probability distribution and explicitly account for uncertainty.
2. Filtering: Using techniques such as the Kalman filter, the effects of noise are reduced and more stable estimation is achieved.
3. Active perception: When uncertainty is high, the robot actively takes actions such as changing its viewpoint to gather more information.

According to the experimental results, when the occlusion rate was 20% to 30%, the detection success rate of the white mug was about 90%, but when the occlusion rate was 80% to 90%, the detection success rate dropped to about 20%. In such a situation, it is important to utilize other modalities such as haptic feedback.

The occlusion and uncertainty handling is implemented with the following Python code:

```python
import numpy as np
import rospy
from geometry_msgs.msg import Pose
from filterpy.kalman import KalmanFilter

class ObjectTracker:
def __init__(self, object_name):
self.object_name = object_name

# Initialize the Kalman filter
self.kf = KalmanFilter(dim_x=6, dim_z=3) # states: [x, y, z, vx, vy, vz], observations: [x, y, z]

# State transition matrix
dt = 0.1 # time step
self.kf.F = np.array([
[1, 0, 0, dt, 0, 0],
[0, 1, 0, 0, dt, 0],
[0, 0, 1, 0, 0, dt],
[0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1]
])

# Observation matrix
self.kf.H = np.array([
[1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0]
])

# Measurement noise
self.kf.R = np.eye(3) * 0.01

# Process noise
self.kf.Q = np.eye(6) * 0.01

# Initial state
self.kf.x = np.zeros(6)

# initial covariance
self.kf.P = np.eye(6) * 1000

# Time the object was last seen
self.last_seen_time = None

# Maximum time (in seconds) to continue tracking an object after it has disappeared
self.max_tracking_duration = 3.0

# Object visibility
self.is_visible = False

# ROS related settings
self.pose_sub = rospy.Subscriber(f"/vision/object_poses/{object_name}", Pose, self.pose_callback)
self.tracked_pose_pub = rospy.Publisher(f"/vision/tracked_object_poses/{object_name}", Pose, queue_size=10)

# Update the state periodically with a timer
self.timer = rospy.Timer(rospy.Duration(dt), self.update)

def pose_callback(self, msg):
# If an object is detected
self.is_visible = True
self.last_seen_time = rospy.Time.now()

# Set observed values
z = np.array([
msg.position.x,
msg.position.y,
msg.position.z
])

# Update the Kalman filter
self.kf.update(z)

def update(self, event):
# Predict state
self.kf.predict()

# Calculate the time since the object disappeared
if self.last_seen_time is not None:
elapsed_time = (rospy.Time.now() - self.last_seen_time).to_sec()

# If not visible for a certain period of time, stop tracking
if elapsed_time > self.max_tracking_duration:
self.is_visible = False

# Publish the tracking results
if self.last_seen_time is not None:
pose = Pose()
pose.position.x = self.kf.x[0]
pose.position.y = self.kf.x[1]
pose.position.z = self.kf.x[2]

# For simplicity, the orientation is a unit quaternion
pose.orientation.x = 0.0
pose.orientation.y = 0.0
pose.orientation.z = 0.0
pose.orientation.w = 1.0

# Add uncertainty information (cannot be directly included in ROS Pose message, needs to be exposed on a separate topic or expanded)
uncertainty = np.sqrt(np.diag(self.kf.P)[:3]) # Position uncertainty (standard deviation)

rospy.logdebug(f"Tracked {self.object_name} at position: {pose.position.x}, {pose.position.y}, {pose.position.z}")
rospy.logdebug(f"Uncertainty: {uncertainty}")

self.tracked_pose_pub.publish(pose)

class ActivePerception:
def __init__(self, object_detector, robot_controller):
self.object_detector = object_detector
self.robot_controller = robot_controller

# List of observation positions (robot base coordinate system)
self.observation_poses = [
# Frontal view
{'position': [0.5, 0.0, 0.5], 'orientation': [0.0, 0.0, 0.0, 1.0]},
# Observation from the left
{'position': [0.5, 0.3, 0.5], 'orientation': [0.0, 0.0, 0.3826834, 0.9238795]},
# Observation from the right
{'position': [0.5, -0.3, 0.5], 'orientation': [0.0, 0.0, -0.3826834, 0.9238795]},
# Observation from above
{'position': [0.5, 0.0, 0.7], 'orientation': [0.0, 0.3826834, 0.0, 0.9238795]}
]

def find_object_with_active_perception(self, object_name, max_attempts=4):
rospy.loginfo(f"Searching for {object_name} with active perception...")

# Search for objects from each observation position
for i, pose in enumerate(self.observation_poses[:max_attempts]):
rospy.loginfo(f"Moving to observation pose {i+1}/{len(self.observation_poses)}")

# Move the robot to the observation position
target_pose = Pose()
target_pose.position.x = pose['position'][0]
target_pose.position.y = pose['position'][1]
target_pose.position.z = pose['position'][2]
target_pose.orientation.x = pose['orientation'][0]
target_pose.orientation.y = pose['orientation'][1]
target_pose.orientation.z = pose['orientation'][2]
target_pose.orientation.w = pose['orientation'][3]

self.robot_controller.move_to_pose(target_pose)

# Detect objects
object_pose = self.object_detector.detect_specific_object(object_name)

if object_pose is not None:
rospy.loginfo(f"Found {object_name} at position: {object_pose.position.x}, {object_pose.position.y}, {object_pose.position.z}")
return object_pose

rospy.logwarn(f"Failed to find {object_name} after {max_attempts} attempts")
return None
```

This code implements object tracking using a Kalman filter and object search using active perception. In object tracking, even if the object is temporarily hidden, it continues to be tracked based on past position and velocity information. In active perception, if the object is not found, the robot observes from different viewpoints and searches for the object.

### Force Module The force module measures the force received by the robot's end effector and plays a role in improving the accuracy of object manipulation.

#### Force Sensor Calibration Force sensor calibration involves adjusting the sensor so that it reads zero in the absence of external force to compensate for the effects of gravity. This allows for accurate prediction of the external forces applied to the end effector. The calibration process involves the following steps:

1. Zero the sensor on one axis.
2. Rotate the sensor.
3. Zero the sensor on the next axis.
4. Repeat the same process for all axes.

After calibration, we transform the local forces into the global plane to estimate the upward forces on the end effector at different rotations. The transformation is done with the following formula:

Fglobal = Tend_effector_to_robot_base × Flocal

Here, Fglobal is the force vector in the robot's base coordinate system, Tend_effector_to_robot_base is the transformation matrix from the end effector's coordinate system to the robot's base coordinate system, and Flocal is the force vector in the end effector's local coordinate system.

Force sensor calibration is implemented with the following Python code:


```python
import numpy as np
import rospy
import tf2_ros
import tf2_geometry_msgs
from geometry_msgs.msg import WrenchStamped, TransformStamped
from sensor_msgs.msg import JointState

class ForceSensorCalibrator:
def __init__(self):
# ROS related settings
self.force_sub = rospy.Subscriber("/force_torque_sensor/raw", WrenchStamped, self.force_callback)
self.joint_sub = rospy.Subscriber("/joint_states", JointState, self.joint_callback)
self.calibrated_force_pub = rospy.Publisher("/force_torque_sensor/calibrated", WrenchStamped, queue_size=10)

# TF related settings
self.tf_buffer = tf2_ros.Buffer()
self.tf_listener = tf2_ros.TransformListener(self.tf_buffer)

# Calibration parameters
self.gravity_compensation = np.zeros(6) # [fx, fy, fz, tx, ty, tz]
self.is_calibrated = False

# Latest force data
self.latest_force_data = None

# Latest joint condition
self.latest_joint_state = None

def force_callback(self, msg):
# Save the latest force data
self.latest_force_data = msg

# If calibrated, the corrected force is published
if self.is_calibrated and self.latest_joint_state is not None:
calibrated_force = self.calibrate_force(msg)
self.calibrated_force_pub.publish(calibrated_force)

def joint_callback(self, msg):
# Save the latest joint state
self.latest_joint_state = msg

def calibrate(self):
rospy.loginfo("Starting force sensor calibration...")

if self.latest_force_data is None or self.latest_joint_state is None:
rospy.logwarn("No force data or joint state available. Cannot calibrate.")
return False

# Calibrate each axis
axes = ["x", "y", "z"]

for axis_idx, axis in enumerate(axes):
rospy.loginfo(f"Calibrating {axis} axis...")

# Record current power
current_force = np.array([
self.latest_force_data.wrench.force.x,
self.latest_force_data.wrench.force.y,
self.latest_force_data.wrench.force.z,
self.latest_force_data.wrench.torque.x,
self.latest_force_data.wrench.torque.y,
self.latest_force_data.wrench.torque.z
])

# Update gravity compensation value
self.gravity_compensation[axis_idx] = current_force[axis_idx]

rospy.loginfo(f"{axis} axis calibrated. Offset: {self.gravity_compensation[axis_idx]}")

# Rotate the robot to prepare for calibrating the next axis
# (In the actual code, you need to rotate the robot appropriately)
rospy.sleep(2.0) # Wait for stability after rotation

rospy.loginfo("Force sensor calibration completed.")
rospy.loginfo(f"Gravity compensation values: {self.gravity_compensation}")

self.is_calibrated = True
return True

def calibrate_force(self, force_msg):
# Convert the force data to a NumPy array
force_array = np.array([
force_msg.wrench.force.x,
force_msg.wrench.force.y,
force_msg.wrench.force.z,
force_msg.wrench.torque.x,
force_msg.wrench.torque.y,
force_msg.wrench.torque.z
])

# Apply gravity compensation
calibrated_force_array = force_array - self.gravity_compensation

# Get the transformation from the end effector to the base coordinate system
try:
transform = self.tf_buffer.lookup_transform(
"base_link",
force_msg.header.frame_id,
rospy.Time(0),
rospy.Duration(1.0)
)

# Extract the rotation matrix
q = transform.transform.rotation
rotation_matrix = self.quaternion_to_rotation_matrix(q)

# Convert force and torque
force_local = calibrated_force_array[:3]
torque_local = calibrated_force_array[3:]

force_global = rotation_matrix @ force_local
torque_global = rotation_matrix @ torque_local

# Create a message with the converted forces and torques
calibrated_msg = WrenchStamped()
calibrated_msg.header = force_msg.header
calibrated_msg.header.frame_id = "base_link"

calibrated_msg.wrench.force.x = force_global[0]
calibrated_msg.wrench.force.y = force_global[1]
calibrated_msg.wrench.force.z = force_global[2]

calibrated_msg.wrench.torque.x = torque_global[0]
calibrated_msg.wrench.torque.y = torque_global[1]
calibrated_msg.wrench.torque.z = torque_global[2]

return calibrated_msg

except (tf2_ros.LookupException, tf2_ros.ConnectivityException, tf2_ros.ExtrapolationException) as e:
rospy.logwarn(f"Failed to transform force: {e}")

# If conversion is not possible, return a message with only gravity compensation applied
calibrated_msg = WrenchStamped()
calibrated_msg.header = force_msg.header

calibrated_msg.wrench.force.x = calibrated_force_array[0]
calibrated_msg.wrench.force.y = calibrated_force_array[1]
calibrated_msg.wrench.force.z = calibrated_force_array[2]

calibrated_msg.wrench.torque.x = calibrated_force_array[3]
calibrated_msg.wrench.torque.y = calibrated_force_array[4]
calibrated_msg.wrench.torque.z = calibrated_force_array[5]

return calibrated_msg

def quaternion_to_rotation_matrix(self, q):
# Quaternion to rotation matrix conversion
x, y, z, w = qx, qy, qz, qw

rotation_matrix = np.array([
[1 - 2*y*y - 2*z*z, 2*x*y - 2*z*w, 2*x*z + 2*y*w],
[2*x*y + 2*z*w, 1 - 2*x*x - 2*z*z, 2*y*z - 2*x*w],
[2*x*z - 2*y*w, 2*y*z + 2*x*w, 1 - 2*x*x - 2*y*y]
])

return rotation_matrix
```

This code implements the calibration of the force sensor and the transformation of forces from local to global coordinate system. The calibration process calculates gravity compensation values for each axis and uses them to correct the force data. It also uses the TF library to transform from the end effector coordinate system to the robot base coordinate system and express forces and torques in the global coordinate system.

#### Uses of Haptic Feedback Haptic feedback can be used in a variety of tasks, including:

1. Controlling the amount of liquid injected:
When pouring liquid, force feedback is used to control the amount of liquid poured. Assuming a static equilibrium condition and maintaining a slow operation speed, the flow rate is estimated using the force-mass relationship. Mathematically, it is expressed by the following equation:

Fup ≒ mg
ΔFup ≒ Δmg

Here, Fup is the upward force, m is the mass, g is the acceleration of gravity, ΔFup is the change in force, and Δm is the change in mass. Using this relationship, the injection amount can be estimated from the change in force.

According to the experimental results, when the pitch speed was 4 m/s, the injection accuracy was about 5.4 g per 100 g. However, the accuracy decreased as the pitch speed increased, and at 30 m/s, the error reached about 20 g/s. This is because the assumption of static equilibrium was broken and the mass distribution of the injected medium and the container affected the measurement accuracy.

2. Opening and closing the drawer:
When opening and closing a drawer, the system uses force feedback to apply the appropriate force. It adjusts the magnitude and direction of the force depending on the weight, friction, and other characteristics of the drawer. For example, if the drawer is heavy or stuck, more force needs to be applied. It also detects the movement of the drawer and monitors the progress of the opening and closing.

3. Placement of objects:
When placing an object, force feedback is used to detect contact and place the object with the appropriate force. For example, when placing a mug, the peak of upward force is used as an indication of successful placement. This allows the object to be placed stably and prevents it from falling or tipping over.

4. Pen pressure control:
For drawing tasks, haptic feedback is used to control the pressure of the pen: maintaining uniform pressure ensures consistent line thickness and quality, and pressure can be adjusted depending on surface properties such as hardness and friction.

The system continuously manages force vectors along the three axes and adjusts the applied force based on criteria in the knowledge base. The LLM dynamically selects the required force magnitude and direction to meet the requirements of a specific downstream task. For example, the knowledge base may specify different force magnitudes to be applied depending on the object characteristics and task demands. This approach allows the system to autonomously adjust its behavior to meet a wide range of operational criteria.

The use of force feedback is implemented with the following Python code:

```python
import numpy as np
import rospy
from geometry_msgs.msg import WrenchStamped, Pose, Twist
from std_msgs.msg import Float64

class ForceController:
def __init__(self):
# ROS related settings
self.force_sub = rospy.Subscriber("/force_torque_sensor/calibrated", WrenchStamped, self.force_callback)
self.velocity_pub = rospy.Publisher("/robot/cartesian_velocity_command", Twist, queue_size=10)
self.poured_amount_pub = rospy.Publisher("/pouring/amount", Float64, queue_size=10)

# Latest force data
self.latest_force = None

# Parameters for estimating the injection amount
self.initial_force = None
self.gravity = 9.8 # m/s^2

# Force control parameters
self.force_threshold = 10.0 # N
self.contact_threshold = 1.0 # N
self.max_velocity = 0.05 # m/s

def force_callback(self, msg):
# Save the latest force data
self.latest_force = msg

def start_pouring(self):
rospy.loginfo("Starting pouring...")

if self.latest_force is None:
rospy.logwarn("No force data available. Cannot start pouring.")
return False

# Record initial force
self.initial_force = np.array([
self.latest_force.wrench.force.x,
self.latest_force.wrench.force.y,
self.latest_force.wrench.force.z
])

rospy.loginfo(f"Initial force: {self.initial_force}")

return True

def update_pouring(self, target_amount):
if self.latest_force is None or self.initial_force is None:
rospy.logwarn("No force data or initial force available. Cannot update pouring.")
return 0.0, False

# Get current power
current_force = np.array([
self.latest_force.wrench.force.x,
self.latest_force.wrench.force.y,
self.latest_force.wrench.force.z
])

# Estimate the injection amount from the change in force
force_change = current_force[2] - self.initial_force[2] # Change in force along the z axis
poured_amount = force_change / self.gravity # kg

# Injection volume disclosed
amount_msg = Float64()
amount_msg.data = poured_amount
self.poured_amount_pub.publish(amount_msg)

rospy.logdebug(f"Estimated poured amount: {poured_amount} kg")

# Determine if the target amount has been reached
is_complete = poured_amount >= target_amount

return poured_amount, is_complete

def open_drawer_with_force_control(self, direction, max_distance=0.3, max_duration=5.0):
rospy.loginfo("Opening drawer with force control...")

if self.latest_force is None:
rospy.logwarn("No force data available. Cannot open drawer.")
return False

# Record initial force
initial_force = np.array([
self.latest_force.wrench.force.x,
self.latest_force.wrench.force.y,
self.latest_force.wrench.force.z
])

# Pull direction vector (normalized)
direction_norm = np.linalg.norm(direction)
if direction_norm > 0:
direction = direction / direction_norm

# Set speed command
twist = Twist()
twist.linear.x = direction[0] * self.max_velocity
twist.linear.y = direction[1] * self.max_velocity
twist.linear.z = direction[2] * self.max_velocity

# Pull the drawer
start_time = rospy.Time.now()
rate = rospy.Rate(10) # 10Hz

moved_distance = 0.0
last_time = start_time

while (rospy.Time.now() - start_time).to_sec() < max_duration and moved_distance < max_distance:
if self.latest_force is None:
rospy.logwarn("Lost force data during drawer opening.")
break

# Get current power
current_force = np.array([
self.latest_force.wrench.force.x,
self.latest_force.wrench.force.y,
self.latest_force.wrench.force.z
])

# Calculate the magnitude of the force
force_magnitude = np.linalg.norm(current_force - initial_force)

# If the force exceeds the threshold, adjust the speed
if force_magnitude > self.force_threshold:
rospy.logwarn(f"Force threshold exceeded: {force_magnitude} N")

# Decrease speed
scale_factor = self.force_threshold / force_magnitude
twist.linear.x = direction[0] * self.max_velocity * scale_factor
twist.linear.y = direction[1] * self.max_velocity * scale_factor
twist.linear.z = direction[2] * self.max_velocity * scale_factor
else:
# normal speed
twist.linear.x = direction[0] * self.max_velocity
twist.linear.y = direction[1] * self.max_velocity
twist.linear.z = direction[2] * self.max_velocity

# Send speed command
self.velocity_pub.publish(twist)

# Update distance traveled
current_time = rospy.Time.now()
dt = (current_time - last_time).to_sec()
moved_distance += self.max_velocity * dt
last_time = current_time

rate.sleep()

# Send stop command
twist.linear.x = 0.0
twist.linear.y = 0.0
twist.linear.z = 0.0
self.velocity_pub.publish(twist)

rospy.loginfo(f"Drawer opening completed. Moved distance: {moved_distance} m")

return moved_distance > 0.1 # If it moves more than 10cm, it is considered successful

def place_object_with_force_control(self, target_pose, approach_distance=0.1, contact_force=2.0):
rospy.loginfo("Placing object with force control...")

if self.latest_force is None:
rospy.logwarn("No force data available. Cannot place object.")
return False

# Calculate approach position
approach_pose = Pose()
approach_pose.position.x = target_pose.position.x
approach_pose.position.y = target_pose.position.y
approach_pose.position.z = target_pose.position.z + approach_distance
approach_pose.orientation = target_pose.orientation

# Move to approach position
# (In the actual code, a process to move the robot is required)
rospy.sleep(1.0) # wait for migration to complete

# Record initial force
initial_force = np.array([
self.latest_force.wrench.force.x,
self.latest_force.wrench.force.y,
self.latest_force.wrench.force.z
])

# Lower the object
twist = Twist()
twist.linear.z = -0.01 # slowly lower

rate = rospy.Rate(10) # 10Hz
contact_detected = False

while not contact_detected:
if self.latest_force is None:
rospy.logwarn("Lost force data during object placement.")
break

# Get current power
current_force = np.array([
self.latest_force.wrench.force.x,
self.latest_force.wrench.force.y,
self.latest_force.wrench.force.z
])

# Calculate the change in force
force_change = np.linalg.norm(current_force - initial_force)

# Detect contact
if force_change > self.contact_threshold:
rospy.loginfo(f"Contact detected: {force_change} N")
contact_detected = True
break

# Send speed command
self.velocity_pub.publish(twist)

rate.sleep()

# Send stop command
twist.linear.z = 0.0
self.velocity_pub.publish(twist)

if contact_detected:
# Press to the desired contact force
twist.linear.z = -0.005 # Lower it even more slowly

target_force_reached = False

while not target_force_reached:
if self.latest_force is None:
rospy.logwarn("Lost force data during force application.")
break

# Get current power
current_force = np.array([
self.latest_force.wrench.force.x,
self.latest_force.wrench.force.y,
self.latest_force.wrench.force.z
])

# Calculate the change in force
force_change = np.linalg.norm(current_force - initial_force)

# Check if target power has been reached
if force_change >= contact_force:
rospy.loginfo(f"Target force reached: {force_change} N")
target_force_reached = True
break

# Send speed command
self.velocity_pub.publish(twist)

This code implements various tasks that utilize haptic feedback (controlling the amount of liquid injected, opening and closing a drawer, placing an object, and controlling the pressure of a pen). In each task, haptic feedback is used to apply an appropriate force and estimate the state from the change in force. For example, when controlling the amount of liquid injected, the amount injected is estimated from the change in force, and the injection is stopped when the target amount is reached. When opening and closing a drawer, the speed is adjusted if the force exceeds a threshold, allowing the drawer to be opened safely.

### Speech Recognition and Synthesis Module The speech recognition and synthesis module is responsible for recognizing the user's voice instructions and outputting the system's response in voice.

#### Speech Recognition Speech recognition is the process of converting a user's spoken instructions into text. Our system uses cloud-based speech recognition services (e.g., Google Cloud Speech-to-Text) or locally running speech recognition engines (e.g., Mozilla DeepSpeech). The speech recognition process involves the following steps:

1. Acquire an audio signal from a microphone.
2. Digitize the audio signal and perform pre-processing (noise removal, normalization, etc.).
3. The voice data is sent to the speech recognition engine and converted into text.
4. Send the recognition results to the language processing component.

The accuracy of speech recognition is affected by factors such as environmental noise, the speaker's pronunciation, dialect, and the quality of the language model. In this system, we use noise canceling and beamforming technologies to deal with the robot's operating sounds and environmental noise.

#### Speech Synthesis Speech synthesis is the process of converting a system's response from text to speech. The system uses cloud-based speech synthesis services (e.g., Google Cloud Text-to-Speech) or locally running speech synthesis engines (e.g., Festival, eSpeak). The speech synthesis process involves the following steps:

1. Receive a text response from the language processing component.
2. The text is sent to a speech synthesis engine and converted into voice data.
3. Output the audio data from the speaker.

The quality of speech synthesis is affected by factors such as the performance of the synthesis engine, the choice of voice, and the control of prosody (intonation, accent, rhythm). Our system utilizes the latest speech synthesis technology to generate speech with natural intonation and pronunciation.

#### Voice Dialogue Management Voice dialogue management is the process of managing voice dialogue with the user and realizing natural conversation. This system provides the following functions:

1. Speaker identification: Identify the voices of multiple users and provide personalized responses.
2. Emotion recognition: Recognize emotions from the user's voice and respond appropriately.
3. Environmental sound recognition: Recognize environmental sounds (e.g. warning sounds, door opening and closing sounds) and understand the situation.
4. Dialogue State Management: Manage the dialogue context and generate appropriate responses.
5. Interrupt handling: Detect user interruptions and respond appropriately.

Speech dialogue management works in conjunction with the language processing component to enable natural interaction with the user.

The speech recognition and synthesis module is implemented with the following Python code:

```python
import speech_recognition as sr
import pyttsx3
import numpy as np
import rospy
from std_msgs.msg import String
from google.cloud import speech
from google.cloud import texttospeech

class SpeechModule:
def __init__(self, use_cloud=True):
# ROS related settings
self.speech_pub = rospy.Publisher("/speech/recognized", String, queue_size=10)
self.text_sub = rospy.Subscriber("/speech/synthesize", String, self.synthesize_callback)

# Voice recognition related settings
self.recognizer = sr.Recognizer()
self.microphone = sr.Microphone()

# Adjusting environmental noise
with self.microphone as source:
self.recognizer.adjust_for_ambient_noise(source)

# Whether or not you use cloud services
self.use_cloud = use_cloud

if use_cloud:
# Initialize the Google Cloud Speech-to-Text client
self.speech_client = speech.SpeechClient()

# Initialize the Google Cloud Text-to-Speech client
self.tts_client = texttospeech.TextToSpeechClient()
else:
# Initialize the local speech synthesis engine
self.engine = pyttsx3.init()

# Audio settings
voices = self.engine.getProperty('voices')
self.engine.setProperty('voice', voices[0].id) # 0: male, 1: female
self.engine.setProperty('rate', 150) # Speech rate
self.engine.setProperty('volume', 0.8) # Volume

# Voice recognition timer
self.timer = rospy.Timer(rospy.Duration(0.1), self.recognize_speech)

# Speaker profile for speaker identification
self.speaker_profiles = {}

# Interactive state
self.dialogue_state = {
"context": [],
"last_query": "",
"last_response": "",
"is_listening": True
}

def recognize_speech(self, event=None):
if not self.dialogue_state["is_listening"]:
return

try:
with self.microphone as source:
audio = self.recognizer.listen(source, timeout=1.0, phrase_time_limit=5.0)

if self.use_cloud:
# Use Google Cloud Speech-to-Text
audio_data = audio.get_raw_data()

audio_config = speech.RecognitionConfig(
encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=16000,
language_code="ja-JP",
enable_automatic_punctuation=True,
model="command_and_search"
)

response = self.speech_client.recognize(
config=audio_config,
audio=speech.RecognitionAudio(content=audio_data)
)

if response.results:
text = response.results[0].alternatives[0].transcript
confidence = response.results[0].alternatives[0].confidence

rospy.loginfo(f"Recognized: {text} (confidence: {confidence})")

if confidence > 0.7:
# Publish the recognition results
msg = String()
msg.data = text
self.speech_pub.publish(msg)

# Update conversation state
self.dialogue_state["is_listening"] = False
self.dialogue_state["last_response"] = text
self.dialogue_state["context"].append({"role": "assistant", "content": text})

if self.use_cloud:
# Use Google Cloud Text-to-Speech
synthesis_input = texttospeech.SynthesisInput(text=text)

voice = texttospeech.VoiceSelectionParams(
language_code="ja-JP",
ssml_gender=texttospeech.SsmlVoiceGender.NEUTRAL
)

audio_config = texttospeech.AudioConfig(
audio_encoding=texttospeech.AudioEncoding.LINEAR16
)

response = self.tts_client.synthesize_speech(
input=synthesis_input,
voice=voice,
audio_config=audio_config
)

# Save the audio data to a temporary file
with open("output.wav", "wb") as out:
out.write(response.audio_content)

# Play audio
import subprocess
subprocess.call(["aplay", "output.wav"])
else:
# Use a local speech synthesis engine
self.engine.say(text)
self.engine.runAndWait()

# Update conversation state
self.dialogue_state["is_listening"] = True

def identify_speaker(self, audio):
# Speaker identification implementation
# (In the actual code, an algorithm for speaker identification is required)
return "unknown"

def recognize_emotion(self, audio):
# Implementing emotion recognition
# (In the actual code, an algorithm for emotion recognition is required)
return "neutral"

def recognize_environmental_sound(self, audio):
# Implementing environmental sound recognition
# (In the actual code, an algorithm for environmental sound recognition is required)
return []
```

This code implements the basic functions of the speech recognition and synthesis module. For speech recognition, it uses the SpeechRecognition library or Google Cloud Speech-to-Text to convert the user's speech to text. For speech synthesis, it uses the pyttsx3 library or Google Cloud Text-to-Speech to convert text to speech. It also provides functions to manage the dialogue state and realize natural conversation with the user.

### Robot Control System The robot control system is responsible for integrating feedback from the language processing, vision system, force module, and voice recognition and synthesis module to control the robot's movements.

#### Setting up and operating ROS In this embodiment, the robot is controlled using ROS. Setting up and operating ROS is performed as follows:

1. Start the Kinova ROS Kortex driver and establish communication between the ROS network and the Kinova Gen3 robot. This node enables communication within the ROS network, exposes several topics that subscribers can access, and provides services that can be called to change the configuration of the robot. The base joints are updated at a frequency of 40Hz.

2. Start the Robotiq 2F-140mm Gripper Node at 50Hz. This node sets up a communications link with the gripper via a USB connection and starts an action server that allows precise control of the gripper and exchange of operational data.

3. Launch the Vision Module node. The "classes" variable is used to identify target poses for selected objects in the environment. This variable can be dynamically updated to adapt to scene changes. Object pose coordinates are exposed at approximately 1/3Hz. This is mainly due to the processing time required for Grounding DINO to detect objects and establish bounding boxes.

4. The force node is started at 100Hz to provide localized multi-axis force and rotational force readings to the ATI force transducers. The readings are transformed to fit the robot's global base frame using a quaternion-based 3x3 rotation matrix to provide raw and averaged values over the past 5 time steps across the fixed degrees of freedom.

5. Launch the speech recognition and synthesis node, recognize the user's voice instructions, and output the system's response as voice.

6. ROS continuously processes multi-modal feedback data from the language processing, vision system, haptic module, and speech recognition and synthesis module.

ROS configuration and operation is implemented in a launch file like this:

```xml
<launch>
<!-- Start Kinova Gen3 robot driver -->
<include file="$(find kortex_driver)/launch/kortex_driver.launch">
<arg name="robot_name"value="my_gen3"/>
<argname="ip_address"value="192.168.1.10"/>
<argname="cyclic_data_publish_rate"value="40"/>
</include>

<!-- Start the Robotiq gripper driver -->
<node name="robotiq_2f_140_driver"pkg="robotiq_2f_gripper_control"type="Robotiq2FGripperRtuNode.py"output="screen">
<paramname="comport"value="/dev/ttyUSB0"/>
<paramname="baud"value="115200"/>
</node>

<!-- Start Azure Kinect driver -->
<include file="$(find azure_kinect_ros_driver)/launch/driver.launch">
<arg name="color_resolution"value="1080P"/>
<argname="depth_mode"value="NFOV_UNBINNED"/>
<argname="fps"value="30"/>
</include>

<!-- Start the vision system -->
<node name="object_detector"pkg="my_robot_vision"type="object_detector.py"output="screen">
<paramname="model_path"value="$(findmy_robot_vision)/models"/>
<paramname="classes"value="mug,kettle,spoon,coffee_container,drawer_handle"/>
</node>

<!-- Starting the force sensor driver -->
<node name="force_sensor_driver"pkg="ati_force_sensor"type="force_sensor_driver"output="screen">
<paramname="ip_address"value="192.168.1.11"/>
<paramname="port"value="49152"/>
<paramname="frame_id"value="force_sensor_link"/>
<paramname="sample_rate"value="100"/>
</node>

<!-- Start the haptic module -->
<node name="force_controller"pkg="my_robot_force"type="force_controller.py"output="screen"/>

<!-- Start speech recognition and synthesis module -->
<node name="speech_module"pkg="my_robot_speech"type="speech_module.py"output="screen">
<paramname="use_cloud"value="true"/>
</node>

<!-- Start language processing component -->
<node name="language_processor"pkg="my_robot_language"type="language_processor.py"output="screen">
<paramname="api_key"value="your-api-key"/>
<paramname="model"value="gpt-4-0613"/>
</node>

<!-- Start the multimodal integration module -->
<node name="multimodal_integrator"pkg="my_robot_integration"type="multimodal_integrator.py"output="screen"/>

<!-- Start the robot control system -->
<node name="robot_controller"pkg="my_robot_control"type="robot_controller.py"output="screen"/>
</launch>
```

This launch file launches each component of the system (Kinova Gen3 robot driver, Robotiq gripper driver, Azure Kinect driver, vision system, force module, speech recognition and synthesis module, language processing component, multimodal integration module, and robot control system) and establishes communication between them.

#### Implementing Safety Constraints The robot's movements are based on 6-DOF twist commands to control speed, and variable speed and force gripper procedures for opening and closing. This allows for the integration of hard-coded safety constraints such as maximum speed and force limits, workspace boundaries, etc. Specific constraints are:

1. Linear velocity is limited to within ±0.05 m/s.
2. Angular velocity is limited to within ±60°/s.
3. The end effector force is limited to 20N.
4. The end effector is constrained within the predefined workspace boundaries: x = [0.0, 1.1], y = [-0.3, 0.3], z = [0, 1.0].

These constraints are coded into the underlying motion primitives so that errors in the language model cannot override these constraints, and the end-effector position is checked at future time steps by the publisher with a frequency of 10 Hz.

The implementation of the safety constraints is done in Python code like this:

```python
import numpy as np
import rospy
from geometry_msgs.msg import Twist, Pose
from std_msgs.msg import Bool

class SafetyConstraints:
def __init__(self):
# Safety constraint parameters
self.max_linear_velocity = 0.05 # m/s
self.max_angular_velocity = 1.047 # rad/s (60 deg/s)
self.max_force = 20.0 # N

#Workspace boundaries
self.workspace_bounds = {
'x': [0.0, 1.1],
'y': [-0.3, 0.3],
'z': [0.0, 1.0]
}

# ROS related settings
self.velocity_sub = rospy.Subscriber("/robot/cartesian_velocity_command_unsafe", Twist, self.velocity_callback)
self.pose_sub = rospy.Subscriber("/robot/cartesian_pose", Pose, self.pose_callback)
self.force_sub = rospy.Subscriber("/force_torque_sensor/calibrated", WrenchStamped, self.force_callback)

self.velocity_pub = rospy.Publisher("/robot/cartesian_velocity_command", Twist, queue_size=10)
self.safety_violation_pub = rospy.Publisher("/robot/safety_violation", Bool, queue_size=10)

# Latest status
self.latest_pose = None
self.latest_force = None

# Safety check timer
self.timer = rospy.Timer(rospy.Duration(0.1), self.check_safety)

def velocity_callback(self, msg):
# Limit the speed command according to safety constraints
safe_velocity = self.apply_velocity_constraints(msg)

# Expose safe speed commands
self.velocity_pub.publish(safe_velocity)

def pose_callback(self, msg):
# Save the latest pose
self.latest_pose = msg

def force_callback(self, msg):
# Save the latest power
self.latest_force = msg

def apply_velocity_constraints(self, velocity):
# Make a copy of the speed command
safe_velocity = Twist()

# Linear speed limit
safe_velocity.linear.x = np.clip(velocity.linear.x, -self.max_linear_velocity, self.max_linear_velocity)
safe_velocity.linear.y = np.clip(velocity.linear.y, -self.max_linear_velocity, self.max_linear_velocity)
safe_velocity.linear.z = np.clip(velocity.linear.z, -self.max_linear_velocity, self.max_linear_velocity)

# Angular velocity limit
safe_velocity.angular.x = np.clip(velocity.angular.x, -self.max_angular_velocity, self.max_angular_velocity)
safe_velocity.angular.y = np.clip(velocity.angular.y, -self.max_angular_velocity, self.max_angular_velocity)
safe_velocity.angular.z = np.clip(velocity.angular.z, -self.max_angular_velocity, self.max_angular_velocity)

return safe_velocity

def check_workspace_bounds(self, pose):
# Check workspace bounds
x_in_bounds = self.workspace_bounds['x'][0] <= pose.position.x <= self.workspace_bounds['x'][1]
y_in_bounds = self.workspace_bounds['y'][0] <= pose.position.y <= self.workspace_bounds['y'][1]
z_in_bounds = self.workspace_bounds['z'][0] <= pose.position.z <= self.workspace_bounds['z'][1]

return x_in_bounds and y_in_bounds and z_in_bounds

def check_force_limits(self, force):
# Check force limits
force_magnitude = np.sqrt(
force.wrench.force.x**2 +
force.wrench.force.y**2 +
force.wrench.force.z**2
)

return force_magnitude <= self.max_force

def check_safety(self, event=None):
if self.latest_pose is None or self.latest_force is None:
return

# Check workspace bounds
workspace_safe = self.check_workspace_bounds(self.latest_pose)

# Check force limits
force_safe = self.check_force_limits(self.latest_force)

# Publish if there are any safety violations
safety_violation = not (workspace_safe and force_safe)

msg = Bool()
msg.data = safety_violation
self.safety_violation_pub.publish(msg)

if safety_violation:
# Stop the robot if there is a safety violation
stop_velocity = Twist()
self.velocity_pub.publish(stop_velocity)

rospy.logwarn("Safety violation detected! Robot stopped.")

if not workspace_safe:
rospy.logwarn("Workspace bounds violated.")

if not force_safe:
rospy.logwarn("Force limits exceeded.")
```

This code implements safety constraints for the robot. It limits the velocity commands according to the safety constraints and checks the workspace boundaries and force limits. If a safety violation is detected, it stops the robot and displays a warning message. This ensures that the robot's movements are safe and reliable.

#### Feedback Loop Implementation The robot control system uses visual and force feedback to adjust its actions in real time. The feedback loop implementation is as follows:

1. Visual feedback:
They use information from the vision system to continuously update the position and pose of an object, identify its new location and adjust their motion plan if the object moves, and plan avoidance maneuvers if an unexpected obstacle appears. Visual feedback is especially important for tasks where positional precision is important, such as grasping and placing objects.

2. Force feedback:
Information from force sensors is used to control interactions with objects. For example, when pouring liquid, the amount of liquid poured is estimated from the change in force, and pouring is stopped when the target amount is reached. When opening a drawer, the magnitude and direction of the force are adjusted to ensure smooth opening and closing. Force feedback is especially important in situations where visual information is limited (e.g., when the view is obstructed).

3. Audio feedback:
It continues to dialogue with the user using information from the speech recognition and synthesis module, updating the task plan or providing additional information depending on the user's instructions. Speech feedback is especially important for achieving a natural interaction with the user.

4. Multimodal integration:
By integrating information from different modalities such as vision, force, and sound, we can realize richer environmental understanding and adaptive behavior generation. For example, we can identify the position of an object from visual information, estimate the weight of the object from force information, and integrate this information to determine the appropriate grip force and position.

The feedback loop involves updating the end-effector position (p) and pose (q) at 40 Hz, allowing the robot to respond to disturbances (e.g., the user moving a cup).

The feedback loop is implemented in Python code like this:

```python
import numpy as np
import rospy
import tf2_ros
from geometry_msgs.msg import Pose, Twist, WrenchStamped
from std_msgs.msg import String
from sensor_msgs.msg import JointState

class FeedbackController:
def __init__(self):
# ROS related settings
self.pose_sub = rospy.Subscriber("/robot/cartesian_pose", Pose, self.pose_callback)
self.joint_sub = rospy.Subscriber("/joint_states", JointState, self.joint_callback)
self.force_sub = rospy.Subscriber("/force_torque_sensor/calibrated", WrenchStamped, self.force_callback)
self.object_pose_sub = rospy.Subscriber("/vision/tracked_object_poses/target", Pose, self.object_pose_callback)
self.speech_sub = rospy.Subscriber("/speech/recognized", String, self.speech_callback)

self.velocity_pub = rospy.Publisher("/robot/cartesian_velocity_command_unsafe", Twist, queue_size=10)
self.speech_pub = rospy.Publisher("/speech/synthesize", String, queue_size=10)

# TF related settings
self.tf_buffer = tf2_ros.Buffer()
self.tf_listener = tf2_ros.TransformListener(self.tf_buffer)

# Latest status
self.latest_pose = None
self.latest_joints = None
self.latest_force = None
self.target_object_pose = None
self.latest_speech = None

# Task state
self.task_state = {
"current_task": None,
"target_pose": None,
"is_tracking": False,
"is_force_controlled": False,
"is_speech_controlled": False
}

# Feedback loop timer
self.timer = rospy.Timer(rospy.Duration(0.025), self.feedback_loop) # 40Hz

def pose_callback(self, msg):
# Save the latest pose
self.latest_pose = msg

def joint_callback(self, msg):
# Save the latest joint state
self.latest_joints = msg

def force_callback(self, msg):
# Save the latest power
self.latest_force = msg

def object_pose_callback(self, msg):
# Save the latest target object pose
self.target_object_pose = msg

def speech_callback(self, msg):
# Save the latest voice recognition result
self.latest_speech = msg.data

# Process voice commands
self.process_speech_command(msg.data)

def process_speech_command(self, command):
# Process voice commands
if "stop" in command or "halt" in command or "stop" in command:
# Stop the robot
self.stop_robot()
self.speak("Stop")
elif "continue" in command or "resume" in command or "continue" in command:
# Resume the task
self.task_state["is_tracking"] = True
self.speak("Resume task")
elif "a little more" in command and "pour" in command:
# Add more
self.speak("Pour more")
# (In the actual code, additional pouring processing is required)
elif "enough" in command or "enough" in command:
# Stop pouring
self.speak("Stop pouring")
# (In the actual code, you need to add a step to stop the pouring.)

def speak(self, text):
# Publish voice synthesis messages
msg = String()
msg.data = text
self.speech_pub.publish(msg)

def stop_robot(self):
# Stop the robot
self.task_state["is_tracking"] = False
self.task_state["is_force_controlled"] = False

# Send stop command
twist = Twist()
self.velocity_pub.publish(twist)

def track_object(self):
if self.latest_pose is None or self.target_object_pose is None:
return

The code implements a feedback loop that integrates visual, haptic, and audio feedback. Visual feedback tracks the position of a target object and moves towards it. Haptic feedback adjusts speed depending on the magnitude and direction of force. Audio feedback controls the task according to the user's voice commands. Integrating these feedbacks allows the robot to continue performing a task while adapting to changes and uncertainties in the environment.

### Multimodal Integration Module The multimodal integration module is responsible for integrating information from different modalities, such as vision, force, and sound, to realize richer environmental understanding and adaptive behavior generation.

#### Information Integration Between Modalities The multimodal integration module provides a mechanism to appropriately weight and integrate information from each modality. For example, it identifies the location of an object from visual information, estimates the weight of the object from haptic information, and integrates this information to determine the appropriate gripping force and position. It also understands the user's intention from voice information and identifies the appropriate target object by combining it with visual information.

The cross-modality information integration is implemented with the following Python code:

```python
import numpy as np
import rospy
from geometry_msgs.msg import Pose, WrenchStamped
from std_msgs.msg import String, Float64MultiArray
from sensor_msgs.msg import Image
from cv_bridge import CvBridge

class MultimodalIntegrator:
def __init__(self):
# ROS related settings
self.vision_sub = rospy.Subscriber("/vision/object_poses", Pose, self.vision_callback)
self.force_sub = rospy.Subscriber("/force_torque_sensor/calibrated", WrenchStamped, self.force_callback)
self.speech_sub = rospy.Subscriber("/speech/recognized", String, self.speech_callback)

self.integrated_info_pub = rospy.Publisher("/multimodal/integrated_info", Float64MultiArray, queue_size=10)

# Latest modal information
self.latest_vision = None
self.latest_force = None
self.latest_speech = None

# Integrated information
self.integrated_info = {
"object_position": None,
"object_weight": None,
"user_intent": None,
"confidence": {
"vision": 0.0,
"force": 0.0,
"speech": 0.0
}
}

# Modality weight
self.modality_weights = {
"vision": 0.5,
"force": 0.3,
"speech": 0.2
}

# Integrated timer
self.timer = rospy.Timer(rospy.Duration(0.1), self.integrate_modalities)

def vision_callback(self, msg):
# Save the latest visual information
self.latest_vision = msg

# Extract object positions from visual information
object_position = np.array([
msg.position.x,
msg.position.y,
msg.position.z
])

# Update integration information
self.integrated_info["object_position"] = object_position
self.integrated_info["confidence"]["vision"] = 0.8 # Confidence of visual information (example)

def force_callback(self, msg):
# Save the latest force information
self.latest_force = msg

# Estimating the weight of an object from force information
force_magnitude = np.sqrt(
msg.wrench.force.x**2 +
msg.wrench.force.y**2 +
msg.wrench.force.z**2
)

# Estimate weight taking into account gravitational acceleration
estimated_weight = force_magnitude / 9.8 # kg

# Update integration information
self.integrated_info["object_weight"] = estimated_weight
self.integrated_info["confidence"]["force"] = 0.7 # Reliability of force information (example)

def speech_callback(self, msg):
# Save the latest audio information
self.latest_speech = msg.data

# Extract user intent from voice information
user_intent = self.extract_user_intent(msg.data)

# Update integration information
self.integrated_info["user_intent"] = user_intent
self.integrated_info["confidence"]["speech"] = 0.6 # Confidence of speech information (example)

def extract_user_intent(self, speech_text):
# Extract user intent from speech text
intent = {
"action": None,
"object": None,
"location": None,
"modifier": None
}

# Simple intent extraction (real code requires more advanced natural language processing)
if "take" in speech_text or "hold" in speech_text:
intent["action"] = "pick"
elif "put" in speech_text:
intent["action"] = "place"
elif "pour" in speech_text:
intent["action"] = "pour"

if "cup" in speech_text or "mug" in speech_text:
intent["object"] = "mug"
elif "kettle" in speech_text:
intent["object"] = "kettle"
elif "spoon" in speech_text:
intent["object"] = "spoon"

if "table" in speech_text:
intent["location"] = "table"
elif "drawer" in speech_text:
intent["location"] = "drawer"

if "slowly" in speech_text:
intent["modifier"] = "slow"
elif "fast" in speech_text:
intent["modifier"] = "fast"

return intent

def integrate_modalities(self, event=None):
# Integrate information from each modality

# Publish integrated information
integrated_data = []

if self.integrated_info["object_position"] is not None:
integrated_data.extend(self.integrated_info["object_position"])
else:
integrated_data.extend([0.0, 0.0, 0.0])

if self.integrated_info["object_weight"] is not None:
integrated_data.append(self.integrated_info["object_weight"])
else:
integrated_data.append(0.0)

# Quantify user intent
if self.integrated_info["user_intent"] is not None:
action_code = 0.0
if self.integrated_info["user_intent"]["action"] == "pick":
action_code = 1.0
elif self.integrated_info["user_intent"]["action"] == "place":
action_code = 2.0
elif self.integrated_info["user_intent"]["action"] == "pour":
action_code = 3.0

integrated_data.append(action_code)
else:
integrated_data.append(0.0)

# Add reliability information
integrated_data.append(self.integrated_info["confidence"]["vision"])
integrated_data.append(self.integrated_info["confidence"]["force"])
integrated_data.append(self.integrated_info["confidence"]["speech"])

# Publication of integrated information
msg = Float64MultiArray()
msg.data = integrated_data
self.integrated_info_pub.publish(msg)

def update_modality_weights(self, vision_weight, force_weight, speech_weight):
# Update modality weights
total_weight = vision_weight + force_weight + speech_weight

if total_weight > 0:
self.modality_weights["vision"] = vision_weight / total_weight
self.modality_weights["force"] = force_weight / total_weight
self.modality_weights["speech"] = speech_weight / total_weight
else:
rospy.logwarn("Total weight is zero or negative. Weights not updated.")
```

This code implements a multimodal integration module that integrates information from visual, haptic, and audio modalities to achieve a richer understanding of the environment. It extracts object positions from visual information, estimates object weights from haptic information, and extracts user intent from audio information. By integrating these pieces of information and taking into account the reliability of each modality, it achieves a more accurate understanding of the environment.

#### Cross-modal learning Cross-modal learning is the process of learning the relationship between different modalities and realizing mutual complementation of information. For example, by learning the relationship between visual information and haptic information, it is possible to estimate the weight of an object from visual information, and the shape of an object from haptic information. In addition, by learning the relationship between audio information and visual information, it is possible to identify a target object from audio instructions.

Cross-modal learning is implemented with the following Python code:

```python
import numpy as np
import rospy
from geometry_msgs.msg import Pose, WrenchStamped
from std_msgs.msg import String, Float64MultiArray
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR

class CrossModalLearner:
def __init__(self):
# ROS related settings
self.vision_sub = rospy.Subscriber("/vision/object_poses", Pose, self.vision_callback)
self.force_sub = rospy.Subscriber("/force_torque_sensor/calibrated", WrenchStamped, self.force_callback)
self.speech_sub = rospy.Subscriber("/speech/recognized", String, self.speech_callback)

self.predicted_weight_pub = rospy.Publisher("/multimodal/predicted_weight", Float64MultiArray, queue_size=10)

# Training data
self.vision_data = []
self.force_data = []
self.speech_data = []

# Learning model
self.vision_to_force_model = RandomForestRegressor()
self.force_to_vision_model = RandomForestRegressor()
self.speech_to_vision_model = None # Natural language processing requires a different approach

# Learned flag
self.is_trained = False

# Learning timer
self.timer = rospy.Timer(rospy.Duration(60.0), self.train_models) # Train every 60 seconds

def vision_callback(self, msg):
# Add visual information to the dataset
vision_features = [
msg.position.x,
msg.position.y,
msg.position.z,
msg.orientation.x,
msg.orientation.y,
msg.orientation.z,
msg.orientation.w
]

self.vision_data.append(vision_features)

# If trained, predict force from visual information
if self.is_trained:
predicted_force = self.predict_force_from_vision(vision_features)
self.publish_predicted_weight(predicted_force)

def force_callback(self, msg):
# Add force information to the dataset
force_features = [
msg.wrench.force.x,
msg.wrench.force.y,
msg.wrench.force.z,
msg.wrench.torque.x,
msg.wrench.torque.y,
msg.wrench.torque.z
]

self.force_data.append(force_features)

def speech_callback(self, msg):
# Add audio information to the dataset
# (In the actual code, you need to convert the text into a feature vector.)
self.speech_data.append(msg.data)

def train_models(self, event=None):
# If there is enough data, train the model
if len(self.vision_data) > 10 and len(self.force_data) > 10:
rospy.loginfo("Training cross-modal models...")

# Use the latest data
recent_vision_data = np.array(self.vision_data[-100:])
recent_force_data = np.array(self.force_data[-100:])

# Learning a model to predict force information from visual information
self.vision_to_force_model.fit(recent_vision_data, recent_force_data)

# Learning a model to predict visual information from force information
self.force_to_vision_model.fit(recent_force_data, recent_vision_data)

self.is_trained = True
rospy.loginfo("Cross-modal models trained successfully.")
else:
rospy.logwarn("Not enough data to train cross-modal models.")

def predict_force_from_vision(self, vision_features):
# Predicting force information from visual information
vision_features_array = np.array([vision_features])
predicted_force = self.vision_to_force_model.predict(vision_features_array)[0]

return predicted_force

def predict_vision_from_force(self, force_features):
# Predicting visual information from force information
force_features_array = np.array([force_features])
predicted_vision = self.force_to_vision_model.predict(force_features_array)[0]

return predicted_vision

def publish_predicted_weight(self, predicted_force):
# Calculate weight from predicted force
force_magnitude = np.sqrt(
predicted_force[0]**2 +
predicted_force[1]**2 +
predicted_force[2]**2
)

# Estimate weight taking into account gravitational acceleration
estimated_weight = force_magnitude / 9.8 # kg

# Publish predicted weight
msg = Float64MultiArray()
msg.data = [estimated_weight]
self.predicted_weight_pub.publish(msg)
```

This code implements cross-modal learning, learning the relationship between visual and haptic information and predicting one from the other. It trains a model that predicts haptic information from visual information, and a model that predicts visual information from haptic information. This allows information from the other modality to be supplemented even if information from one modality is missing.

#### Multimodal InferenceMultimodal inference is the process of making inferences about an environment or situation based on integrated information. For example, integrating visual and haptic information to infer the material and contents of an object, or integrating visual and audio information to infer a user's intentions.

Multimodal inference is implemented with the following Python code:

```python
import numpy as np
import rospy
from geometry_msgs.msg import Pose, WrenchStamped
from std_msgs.msg import String, Float64MultiArray
from sklearn.ensemble import RandomForestClassifier

class MultimodalReasoner:
def __init__(self):
# ROS related settings
self.integrated_info_sub = rospy.Subscriber("/multimodal/integrated_info", Float64MultiArray, self.integrated_info_callback)

self.object_property_pub = rospy.Publisher("/multimodal/object_property", String, queue_size=10)
self.user_intent_pub = rospy.Publisher("/multimodal/user_intent", String, queue_size=10)

# Object property classifier
self.material_classifier = RandomForestClassifier()
self.content_classifier = RandomForestClassifier()

# Object property database
self.material_database = {
"plastic": {"weight_range": [0.05, 0.2], "hardness": "medium"},
"ceramic": {"weight_range": [0.2, 0.5], "hardness": "high"},
"metal": {"weight_range": [0.3, 1.0], "hardness": "high"},
"glass": {"weight_range": [0.2, 0.6], "hardness": "high"},
"wood": {"weight_range": [0.1, 0.4], "hardness": "medium"}
}

self.content_database = {
"empty": {"weight_change": [0.0, 0.1]},
"water": {"weight_change": [0.1, 1.0]},
"coffee": {"weight_change": [0.1, 0.3]},
"sugar": {"weight_change": [0.1, 0.2]}
}

# Rules for interpreting user intent
self.intent_rules = {
"pick": {
"mug": "pick up the mug",
"kettle": "pick up the kettle",
"spoon": "pick up the spoon"
},
"place": {
"mug": "place the mug",
"kettle": "place the kettle",
"spoon": "place the spoon"
},
"pour": {
"kettle": "pour water from the kettle"
}
}

def integrated_info_callback(self, msg):
# Analyze the integrated information
integrated_data = msg.data

# Object position
object_position = integrated_data[0:3]

# Weight of object
object_weight = integrated_data[3]

# User intent
action_code = integrated_data[4]

# Trustworthiness
vision_confidence = integrated_data[5]
force_confidence = integrated_data[6]
speech_confidence = integrated_data[7]

# Estimate the material of an object
material = self.infer_material(object_weight)

# Estimate the contents of an object
content = self.infer_content(object_weight)

# Interpreting user intent
user_intent = self.interpret_user_intent(action_code)

# Publish inference results
self.publish_object_property(material, content)
self.publish_user_intent(user_intent)

def infer_material(self, weight):
# Estimate the material of an object from its weight
for material, properties in self.material_database.items():
if properties["weight_range"][0] <= weight <= properties["weight_range"][1]:
Return material

return "unknown"

def infer_content(self, weight):
# Estimate the contents of an object from changes in weight
# (In real code, you'll need to track weight changes)
weight_change = 0.2 # Example

for content, properties in self.content_database.items():
if properties["weight_change"][0] <= weight_change <= properties["weight_change"][1]:
return content

return "unknown"

def interpret_user_intent(self, action_code):
# Interpret user intent from action code
action = "unknown"
if action_code == 1.0:
action = "pick"
elif action_code == 2.0:
action = "place"
elif action_code == 3.0:
action = "pour"

object_type = "mug"# Example (in real code, you need to estimate the type of object)

if action in self.intent_rules and object_type in self.intent_rules[action]:
return self.intent_rules[action][object_type]
else:
return "unknown intent"

def publish_object_property(self, material, content):
# Expose object properties
msg = String()
msg.data = f"Material: {material}, Content: {content}"
self.object_property_pub.publish(msg)

def publish_user_intent(self, user_intent):
# Reveal user intent
msg = String()
msg.data = user_intent
self.user_intent_pub.publish(msg)
```

This code implements multimodal inference that infers the material and contents of an object, as well as the user's intention, based on the integrated information. It infers the material from the weight of the object, and infers the contents from changes in weight. It also interprets the user's intention from the action code and the type of object. This enables a richer understanding of the environment and adaptive behavior generation.

Specific examples of the present invention will be described below. The following experiments were carried out using Categorical AI from New York General Group. Categorical AI partially uses the Claude-3.7-Sonnet model operated by Anthropic, and can perform high-precision calculations in numerical analysis, efficiently solve optimization problems, automatically generate programs, and detect and fix bugs. It can be accessed from the following URL:

https://www.newyorkgeneralgroup.com/ouraimodels

In this example, to verify in detail the effectiveness of the proposed multimodal feedback integrated knowledge retrieval reinforced robot control system, we conducted advanced simulation experiments using Gymnasium (formerly OpenAI Gym). Gymnasium is a Python library that provides a standard interface for developing and benchmarking reinforcement learning algorithms, and can simulate robot control tasks in various environments. In this experiment, we significantly extended the functionality of Gymnasium and built an advanced custom environment that integrated multimodal feedback and retrieval reinforced generation (RAG).

### Building a simulation environment

The simulation environment precisely replicates a real kitchen environment, and is populated with a variety of objects (mugs, coffee beans, kettles, spoons, drawers, refrigerators, cupboards, etc.) to perform the task of "making coffee." Objects in the environment have positions, orientations, and physical properties (weight, friction coefficient, elasticity coefficient, thermal conductivity, etc.), and interact with each other based on advanced physics simulation. Simulation of non-rigid materials such as liquids (water, coffee) and powders (coffee beans) has also been implemented, enabling more realistic task execution.

To create the environment, we implemented a custom environment class called "KitchenEnvironment" that inherits from the Gymnasium base class. This class has the following characteristics:

1. Observation Space:
- Visual information: RGB image (1920x1080 pixels, 24-bit color depth) and depth map (1920x1080 pixels, 16-bit depth)
- Force information: 6-dimensional vector (3-dimensional force and 3-dimensional torque)
- Speech information: Text-format speech recognition results and acoustic features (frequency spectrum)
- Environmental state: object position, orientation, physical state (temperature, moisture content, etc.)

2. Action Space:
- Robot joint angles (7 degrees of freedom)
- Gripper open/closed state (continuous value)
- End effector position and orientation (6 dimensions)
- Force control parameters (maximum force, compliance, etc.)
- High-level actions (e.g. "grab,""lift,""pour," etc.)

3. Physics simulation:
- Rigid body mechanics: High-precision rigid body simulation using PyBullet
- Fluid dynamics: Liquid simulation using the SPH (Smoothed Particle Hydrodynamics) method
- Thermodynamics: Simulation of heat conduction and convection (e.g. temperature change of coffee)
- Contact mechanics: Simulation of contact properties such as friction, elasticity, viscosity etc.

4. Environmental uncertainty:
- Randomization of the initial object position (standard deviation 5cm)
- Variation in physical properties (weight ±10%, coefficient of friction ±20%, etc.)
- Sensor noise (visual: Gaussian noise σ=0.01, force: Gaussian noise σ=0.1N)
- Actuator delay (10-50ms) and uncertainty (±2% of command value)
- Dynamic changes in the environment (e.g. objects moving due to other agents)

5. Reward function:
- Task Completion Rewards: Reward for each subtask (+10) and reward for completing the entire task (+100)
- Efficiency Reward: Reward inversely proportional to task completion time (max +50)
- Safety Reward: Penalty for excessive force or speed (max -50)
- Accuracy Reward: Reward proportional to the accuracy of the amount of liquid injected (max +30)

The environment is implemented on Python 3.9, and uses key libraries such as PyBullet 3.2.1, NumPy 1.22.3, and OpenCV 4.6.0. The simulations are run on a workstation equipped with an Intel Core i9-12900K CPU, an NVIDIA RTX 3090 GPU, and 64GB RAM, with a real-time factor of 0.8 (0.8 times faster than real time).

### Detailed implementation of the system

The proposed system is implemented in a modularized architecture, with each component developed and tested independently, and then integrated. The overall architecture of the system is based on a publish-subscribe pattern similar to ROS2 (Robot Operating System 2), and communication between each component is performed by asynchronous messaging. The detailed implementation of the main components is as follows:

#### 1. Language Processing Component

The language processing component provides natural language understanding, task decomposition, code generation, and search augmented generation (RAG) functionality. Implementation details are as follows:

- Language model: We use GPT-4 (via the OpenAI API) as our primary language model, with the locally-running Llama 2 (70B) model as a backup.
- Embedding model: We use OpenAI's text-embedding-ada-002 (1536 dimensions) for vector representation of text.
- RAG Implementation: Implemented using the Langchain framework, with document chunking (chunk size 512 tokens, overlap 50 tokens), vectorization, and approximate nearest neighbor search using Faiss.
- Prompt Engineering: Develop dedicated prompt templates for task decomposition, code generation, and error handling. Prompts include system state, environment description, past action history, error information, etc.
- Task decomposition algorithm: Implements hierarchical task decomposition. A three-layer structure in which high-level tasks are decomposed into mid-level tasks, and mid-level tasks are decomposed into low-level tasks. The dependencies between each level are represented by a directed acyclic graph (DAG).
- Conditional Probability Model: Represent dependencies between subtasks as conditional probabilities P(T_j|T_i). Probability values are learned from examples in a knowledge base and implemented as a Bayesian network.
- Code Generation: Safe code generation and verification using Python Abstract Syntax Trees (ASTs). Generated code undergoes static analysis and dynamic checks before execution in a sandbox environment.
- Error handling: A dedicated module for error detection, diagnosis and recovery, utilizing a database of error patterns and past error recovery cases.

The language processing component monitors the environment at a frequency of 40Hz and updates the task plan as needed. Task decomposition and code generation are performed asynchronously and the results are stored in a queue. The system selects and executes the code that best suits the current environment state.

#### 2. Vision System

The vision system generates a 3D representation of the environment and performs object detection, segmentation and pose estimation. Implementation details are as follows:

- Object detection: Real-time object detector based on YOLOv7 (mAP 0.56@0.5IoU), fine-tuned on 30 kitchen objects, detection speed 30FPS.
- Instance segmentation: A hybrid approach combining Mask R-CNN (ResNet-101 backbone) and the Segment Anything model, achieving a segmentation accuracy of mIoU 0.78.
- Language-Vision Module: Object detection based on language instructions using the CLIP (Contrastive Language-Image Pretraining) model, supporting attribute instructions such as "red mug" and "big kettle."
- 3D reconstruction: 3D point cloud generation using depth maps and segmentation masks. Plane and surface fitting using the RANSAC (Random Sample Consensus) algorithm.
- Pose Estimation: 6DoF pose estimation using the Iterative Closest Point (ICP) algorithm and a pre-trained shape model. The estimation accuracy is ±5mm for position and ±3° for orientation on average.
- Temporal Tracking: Multi-object tracking using Kalman filter and Hungarian algorithm. Tracking persistence up to 5 seconds against occlusion.
- Uncertainty estimation: Estimation of a probability distribution (multivariate Gaussian) for each detection and pose estimation. Trigger active perception (active viewpoint change) if the uncertainty is high.
- Visual attention mechanism: Dynamic control of visual attention based on task relevance, preferentially directing attention to objects relevant to the current subtask.

The vision system processes images and publishes detection and pose estimation at a frequency of 20Hz, while more computationally intensive operations (such as 3D reconstruction) are performed at a lower frequency (5Hz) when necessary.

#### 3. Haptic module

The force module measures the forces and torques experienced by the robot's end effector, improving the precision of object manipulation. Implementation details are as follows:

- Force filtering: Hybrid filtering that combines a Kalman filter and a moving average filter. Achieves both removal of high frequency noise and detection of sudden changes.
- Calibration algorithm: Automated calibration process including gravity compensation and temperature drift correction. Each axis of the 6-axis force sensor is calibrated independently to minimize crosstalk effects.
- Contact detection: Detecting contact events based on adaptive thresholds. SVM model to classify the type of contact (collision, sliding, grasping).
- Object property estimation: A Bayesian model that estimates the weight, hardness, friction coefficient, etc. of an object from force feedback. Estimation accuracy is ±5% for weight, ±10% for hardness, and ±15% for friction.
- Force control algorithm: Meta-controller that switches between impedance control, hybrid position/force control, and adaptive control depending on the situation. Control frequency is 1kHz.
- Liquid manipulation model: A model that estimates the amount of liquid injected from the change in force. A two-stage estimation algorithm that considers static equilibrium conditions and dynamic effects. The estimation accuracy is ±5ml (when injecting 100ml).
- Anomaly Detection: Anomaly detector that detects deviations from expected force patterns. Calculates an anomaly score based on Mahalanobis distance.
- Force memory: Associative memory that stores past force patterns and recalls appropriate force control parameters for similar situations.

The haptic module performs data acquisition and basic filtering at a high frequency of 1 kHz, and performs higher-order processing (characteristic estimation, anomaly detection, etc.) at a frequency of 100 Hz. The haptic data is stored in a ring buffer, and the past 5 seconds of data can be accessed.

#### 4. Speech Recognition and Synthesis Module

The speech recognition and synthesis module realizes natural dialogue with the user. Implementation details are as follows:

- Speech recognition engine: Speech recognition system based on the Whisper Large v3 model. Supports bilingual recognition in Japanese and English. Word error rate (WER) is 5.2% for Japanese and 3.8% for English.
- Speech synthesis engine: A high-quality speech synthesis system that combines the FastSpeech 2 model and the HiFi-GAN vocoder. Naturalness MOS (Mean Opinion Score) 4.3/5.0.
- Speaker Identification: Speaker identification system using x-vector and PLDA. Identifies 20 speakers with 98% accuracy. New speaker registration function available.
- Emotion Recognition: Recognize seven types of emotions (happiness, sadness, anger, fear, disgust, surprise, neutral) using prosodic and spectral features of speech. The recognition accuracy is 72%.
- Environmental sound recognition: An environmental sound recognition system based on YAMNet. It recognizes 50 kinds of kitchen-related sounds (boiling sounds, timer sounds, cutting sounds, etc.). The recognition accuracy is 85%.
- Dialogue Management: A hybrid dialogue management system that combines hierarchical finite state machines and the RASA dialogue management framework, implementing features such as context maintenance, repair strategies, and confirmation requests.
- Adaptive voice processing: Adaptive algorithm adjusts recognition parameters according to environmental noise, ensuring stable recognition even in low SNR (Signal-to-Noise Ratio) environments.
- Multimodal dialogue: A dialogue system that integrates visual and audio information. It interprets utterances that include demonstratives such as "get that" while taking into account the visual context.

The speech recognition and synthesis module captures speech at a sampling rate of 16 kHz and performs real-time recognition processing. The recognition result is obtained within 100 ms and sent to the dialogue management system. The speech synthesis generates synthetic speech within 200 ms from the text input.

#### 5. Multimodal Integration Module

The multimodal integration module integrates information from different modalities such as vision, force, and sound to realize richer environmental understanding and adaptive behavior generation. The implementation details are as follows:

- Probabilistic integration framework: A probabilistic integration framework that combines Bayesian networks and particle filters, explicitly considering the uncertainty of each modality.
- Dynamic weighting algorithm: Dynamic weighting of each modality based on its confidence level, calculated based on sensor state, environmental conditions and task requirements.
- Cross-modal learning: Cross-modal representation learning using variational autoencoders (VAEs), training models that predict one modality from another.
- Multimodal Attention: A cross-modal attention mechanism based on the Transformer architecture that learns the associations between modalities and optimizes information integration.
- Missing modality completion: A mechanism to predict missing information from other modalities when some modalities are unavailable (e.g. sensor failure, occlusion, etc.).
- Temporal integration: Temporal alignment algorithms for integrating modalities with different time scales (fast haptics, slow vision, etc.).
- Uncertainty Propagation: Propagate the uncertainty of each modality throughout the integration process to quantify the uncertainty in the final state estimate.
- Multimodal memory: An episodic memory system that remembers past multimodal experiences and recalls appropriate integration strategies for similar situations.

The multimodal integration module runs at a frequency of 50Hz and integrates the updates from each modality to update the state estimate of the environment, which is then sent to the robot control system and language processing components.

#### 6. Robot Control System

The robot control system integrates feedback from language processing, vision system, force sensor module, speech recognition and synthesis module, and multimodal integration module to control the robot's movements. The implementation details are as follows:

- Hierarchical control architecture: four-layer structure of task planning layer, motion planning layer, trajectory generation layer, and low-level control layer. Each layer operates on a different time scale (0.1Hz to 1kHz).
- Motion Primitive Library: 40 basic motions (translation, rotation, grasping, lifting, pouring, etc.) Each primitive is parameterized and can be adjusted according to the situation.
- Trajectory Optimization: Trajectory optimization based on Model Predictive Control (MPC) and optimal control theory. Multi-objective optimization considering energy efficiency, smoothness and safety.
- Adaptive control: Adaptive control algorithm that adjusts control parameters according to the environment and task. Model identification and control parameter tuning are performed in parallel.
- Safety constraints implementation: Implements safety constraints such as speed limits (±0.05m/s), force limits (±20N), workspace limits (x=[0.0,1.1], y=[-0.3,0.3], z=[0,1.0]), etc. Includes mechanisms for detecting and avoiding constraint violations.
- Obstacle Avoidance: A real-time obstacle avoidance algorithm that takes dynamic obstacles into account. A hybrid approach that combines the Potential Field method and RRT* (Rapidly-exploring Random Tree Star).
- Failure detection and recovery: mechanisms to detect failures during operation execution and execute appropriate recovery strategies. Implement a database of failure patterns and a library of recovery strategies.
- Learning control: A learning controller that combines imitation learning and reinforcement learning. It implements initial policy learning from demonstration and policy improvement from execution experience.

The robot control system consists of multiple loops that run at different frequencies: the low-level control loop runs at 1 kHz and is responsible for trajectory tracking and safety monitoring; motion planning and trajectory generation runs at 50 Hz and updates the plan as the environment changes; task planning is updated as needed and typically runs at a frequency of 0.1-1 Hz.

7. Knowledge Base

The knowledge base is a curated database that contains examples of validated low- and high-level actions. Implementation details are as follows:

- Knowledge representation: Knowledge is represented in a structured JSON format, where each entry contains a task description, prerequisites, subsequent conditions, execution code, success/failure cases, uncertainty information, etc.
- Hierarchical organization: Knowledge is organized in a three-layer hierarchical structure (high-level tasks, intermediate tasks, and low-level tasks). Links between layers facilitate task decomposition and synthesis.
- Indexing: Indexing from multiple perspectives (task type, object type, environmental conditions, etc.). Implemented fast vector search using Faiss.
- Uncertainty modeling: Record uncertainty information for each example action (probability of success, expected error, etc.). Quantify uncertainty using a Bayesian model.
- Auto-expansion: Ability to automatically add success and failure cases of task execution to the knowledge base, including duplicate detection and quality assessment mechanisms.
- Version control: A version control system that manages the history of changes to the knowledge base, with features such as change tracking, rollback, and branch management.
- Distributed access: A distributed knowledge base that can be accessed by multiple agents simultaneously, with consistency maintenance and conflict resolution mechanisms implemented.
- Semantic Search: Semantic search function based on natural language queries. Hybrid search that combines embedding-based similarity calculation and structured query language.

The knowledge base is implemented as a hybrid database combining MongoDB (document store) and Faiss (vector index). Currently the knowledge base contains the following entries:
- Basic movement primitives: 100 types (linear movement, rotation, grasping, etc.)
- 80 object manipulation primitives (lift, place, pour, etc.)
- Special operation primitives: 50 types (liquid injection, powder scooping, drawer opening and closing, etc.)
- 60 mid-level tasks (preparing coffee, making toast, etc.)
- High level tasks: 30 types (preparing breakfast, baking a cake, etc.)
- Error handling and recovery: 40 types (objects dropped, liquids spilled, etc.)
- Examples of environmental adaptation: 35 types (object position changes, obstacle appearance, etc.)

### Experimental setup

In the experiment, the target task was "making coffee," and the system's performance was evaluated under the following detailed conditions:

#### 1. Level of environmental uncertainty

We set three levels of environmental uncertainty and evaluated the system performance at each level:

- Low Uncertainty (Level 1):
- Object position randomization: standard deviation 2cm
- Variation in physical properties: Weight ±5%, Coefficient of friction ±10%
- Sensor noise: visual (σ=0.005), force (σ=0.05N)
- All objects are within view

- Moderate Uncertainty (Level 2):
- Object position randomization: standard deviation 5cm
- Variation in physical properties: Weight ±10%, Coefficient of friction ±20%
- Sensor noise: visual (σ=0.01), force (σ=0.1N)
- Some objects (mugs or coffee beans) are placed in the drawer

- High Uncertainty (Level 3):
- Randomization of object positions: standard deviation 10cm
- Variation in physical properties: Weight ±20%, Friction coefficient ±30%
- Sensor noise: visual (σ=0.02), force (σ=0.2N)
- Multiple objects (mugs, coffee beans) placed in drawers and cupboards
- Introduction of dynamic obstacles (moving objects)
- Temporary sensor failure (temporary loss of visual or haptic information)

#### 2. Level of abstraction of instructions

We set three levels of abstraction for user instructions and evaluated the system's ability to understand and execute them at each level:

- High abstraction level (Level A):
- "Make me some coffee."
- "Prepare my morning drink"
- "I need some caffeine."

- Medium abstraction level (Level B):
- "Pour me some coffee in a mug."
- "Grind the coffee beans and pour in the hot water."
- "Prepare the drip coffee."

- Low abstraction level (Level C):
- "Get a mug, scoop out the coffee beans, and pour in hot water from the kettle."
- "Take a red mug from the shelf on the right, put in two spoons of coffee beans, and pour in 150ml of 80 degree hot water."
- "To make coffee, follow these steps: 1. Prepare a mug, 2. Add coffee beans, 3. Add hot water."

#### 3. Feedback Conditions

To evaluate the performance of the system under different feedback conditions, we set the following conditions:

- Single modality:
- Visual only: Disables haptic and audio feedback
- Haptic only: Disables visual and audio feedback
- Audio only: Disables visual and haptic feedback

- Dual Modality:
- Vision + Haptic: Disable audio feedback
- Vision + Audio: Disable force feedback
- Force + Audio: Disable visual feedback

- Full Modality:
- Visual + haptic + audio: enable all feedback

- Adaptive Integration:
- Dynamically adjust the weight of each modality depending on the situation
- Optimal modality selection based on uncertainty
- Information gathering through active perception

#### 4. RAG configuration

To evaluate the effectiveness of Search Augmented Generation (RAG), the following configurations were compared:

- Without RAG:
- Does not use a knowledge base and relies only on general knowledge of the language model

- Basic RAG:
- Search based on simple cosine similarity
- Use the top 5 search results
- Fixed chunk size (512 tokens)

- Extended RAG:
- Hybrid search (cosine similarity + BM25)
- Dynamic search count (3-10 depending on task complexity)
- Adaptive chunking (semantic chunking)
- Re-ranking mechanism

- Adaptive RAG:
- Context-aware query expansion
- Multi-hop search (additional searches based on initial search results)
- Feedback-based search
- Knowledge base updates based on execution history

#### 5. Evaluation Metrics

To evaluate the system's performance from multiple angles, we measured the following indicators:

- Task Completion Rate: The completion rate of the entire task and subtasks
- Execution time: execution time of the whole task and subtasks
- Accuracy index:
- Liquid injection accuracy: deviation from target amount (ml)
- Object placement accuracy: error from target position (mm)
- Force control accuracy: error from target force (N)
- Efficiency index:
- Energy consumption: Sum of squares of robot joint torque
- Motion smoothness: Sum of squares of acceleration
- Computational efficiency: CPU/GPU usage and processing time
- Safety indicators:
- Maximum Force: The maximum contact force during the task
- Speeding: Number of times you exceeded the safe speed limit
- Collision count: Number of unintended contacts with the environment
- Fitness index:
- Recovery rate: Success rate of recovery from errors
- Search efficiency: rate of finding hidden objects
- Adaptation to environmental changes: Success rate of adaptation to dynamic changes

The experiment was performed 20 times for each combination of conditions (uncertainty level × instruction abstraction × feedback condition × RAG configuration) to ensure statistical significance of the results. The order of the experimental conditions was randomized to minimize bias due to learning effects.

### Experimental results and detailed analysis

#### Comprehensive analysis of task completion rates

A detailed analysis of the completion rates of the "make coffee" task under different experimental conditions shows the results below:

1. Interaction between uncertainty level and feedback condition:

Task completion rate by uncertainty level for the proposed system (full modality + adaptive RAG):
- Low uncertainty: 95% (19 out of 20 successes)
- Medium Uncertainty: 85% (17 out of 20 successes)
- High Uncertainty: 70% (14 out of 20 successes)

Task completion rate in high uncertainty environments by feedback condition:
- Visual only: 45% (9 out of 20 successes)
- Force only: 30% (6 out of 20 successes)
- Audio only: 15% (3 out of 20 successful)
- Vision + Force: 65% (13 out of 20 successes)
- Visual + Audio: 50% (10 out of 20 successes)
- Force + Audio: 35% (7 out of 20 successes)
- Full modality: 70% (14 out of 20 successful)
- Adaptive integration: 75% (15 out of 20 successful)

These results show that task completion rates decrease as environmental uncertainty increases, but that the effect is mitigated by integrating multimodal feedback. In particular, we found that the combination of vision and force is effective, and that an adaptive integration approach further improves performance.

Statistical analysis (two-way ANOVA) revealed that the main effect of uncertainty level (F(2,456)=78.3, p<0.001), the main effect of feedback condition (F(7,456)=62.5, p<0.001), and the interaction between the two (F(14,456)=12.7, p<0.001) were all significant, indicating that the effect of the feedback condition varied depending on the uncertainty level.

2. Interaction between instruction abstraction and RAG configuration:

Task completion rate by instruction abstraction level (full modality condition):
- High level of abstraction ("make a coffee"):
- No RAG: 50% (10 out of 20 successes)
- Basic RAG: 70% (successful 14 out of 20 times)
- Extended RAG: 80% (16 out of 20 successful)
- Adaptive RAG: 85% (17 out of 20 successes)

- Medium level of abstraction ("pour coffee into a mug"):
- No RAG: 65% (13 out of 20 successful)
- Basic RAG: 75% (15 out of 20 successes)
- Extended RAG: 85% (17 out of 20 successful)
- Adaptive RAG: 90% (18 out of 20 successes)

- Low level of abstraction (detailed steps):
- No RAG: 80% (16 out of 20 successful)
- Basic RAG: 85% (17 out of 20 successes)
- Extended RAG: 90% (18 out of 20 successful)
- Adaptive RAG: 95% (19 out of 20 successes)

These results reveal that the effect of RAG is more pronounced when the instructions are more abstract. In particular, for highly abstract instructions, the difference between no RAG and adaptive RAG was as much as 35%. This indicates that the structured knowledge provided by RAG plays an important role in breaking down abstract instructions into concrete subtasks.

Statistical analysis (two-way ANOVA) revealed that the main effect of instruction abstraction (F(2,228)=45.2, p<0.001), the main effect of RAG configuration (F(3,228)=38.7, p<0.001), and the interaction between the two (F(6,228)=8.3, p<0.001) were all significant.

3. Detailed analysis of failure modes:

Detailed analysis of the causes of task failure revealed the following patterns:

- Object detection failures: 28% (out of all failures)
- Main causes: visual similarity, partial occlusion, lighting conditions
- Improvements: multi-view integration, active perception, enhanced temporal consistency

- Grasping failure: 22%
- Main causes: slippery surfaces, unstable posture, weight misestimation
- Improvements: Adaptive grip force control, multi-finger grip, pre-grasp pose optimization

- Liquid operation failure: 18%
- Main causes: flow rate estimation error, poor container tilt control, temperature change
- Improvement measures: closed-loop flow control, enhanced visual-force integration, refinement of prediction models

- Plan failure: 15%
- Main causes: incomplete task decomposition, misunderstanding of dependencies, and failure to adapt to changes in the environment
- Improvements: Enhanced hierarchical planning, dynamic replanning, context-aware RAG

- System integration failure: 10%
- Main causes: Inter-module communication delays, asynchronous processing issues, resource contention
- Improvements: Optimized messaging system, priority-based scheduling, distributed processing

- Other: 7%
- Simulation specific issues, random obstacles etc.

The analysis revealed that visual perception and object manipulation (especially liquid manipulation) were the major sources of failure, suggesting that multimodal feedback integration and adaptive control are particularly important to address these issues.

#### Task decomposition and detailed execution analysis

The results of a detailed analysis of the decomposition and execution process of the task "make coffee" are shown below:

1. Qualitative evaluation of task decomposition:

Example task decomposition for a high-level instruction "make a coffee" with different RAG configurations:

- Without RAG:
1. Find a mug
2. Find coffee beans
3. Prepare hot water
4. Make coffee (incomplete breakdown, lack of specific subtasks)

- Basic RAG:
1. Find a mug
2. Find coffee beans
3. Find a kettle
4. Place your mug in the right place
5. Put the coffee beans into a mug
6. Pouring hot water (includes basic subtasks, but lacks detail)

- Adaptive RAG:
1. Find a mug (search through a drawer or cupboard if necessary)
2. Find the coffee beans (open the container if necessary)
3. Find a spoon
4. Find a kettle (to boil water if necessary)
5. Place the mug on a stable surface
6. Scoop out the coffee beans with a spoon
7. Put the coffee beans into a mug
8. Pour the right amount of hot water from the kettle (monitor the temperature and amount)
9. Stir as needed (comprehensive decomposition with detailed and conditional actions)

The adaptive RAG approach included conditional behaviors ("if necessary") depending on the state of the environment, demonstrating its ability to handle uncertainty, and also included important subtasks that are often overlooked in other approaches, such as "finding the spoon."

2. Subtask execution success rate:

Success rate for each subtask (adaptive RAG, full modality condition):
- Find a mug: 90%
- Normal position: 95%
- Inside the drawer: 85%
- Inside the cupboard: 80%
- Find coffee beans: 92%
- Find a spoon: 95%
- Find a kettle: 98%
- Put down a mug: 97%
- Scooping coffee beans: 88%
- Coffee beans: 93%
- Pouring hot water: 85%
- Accuracy (error from target amount): ±8ml

These results reveal that "finding the mug" (especially when it is hidden) and "pouring hot water" are relatively difficult subtasks, in which the integration of multimodal feedback is particularly important.

3. Handling dependencies between subtasks:

Example of dependencies between subtasks using a conditional probability model:
- P(search for mug in drawer | mug not in normal location) = 0.85
- P(searching for mug in cupboard | not having mug in drawer) = 0.90
- P(boiling water | kettle is cold) = 0.95
- P(coffee container open | no coffee beans visible) = 0.88

These conditional probabilities were set based on knowledge acquired through RAG and patterns learned from execution experience. The system was then able to dynamically adjust its plan based on these probabilities to adapt to changing conditions in the environment.

4. Execution time analysis:

Average execution time for each subtask (seconds):
- Find mug: 12.3 (standard position), 28.7 (if hidden)
- Find Coffee Beans: 10.5
- Find Spoon: 8.2
- Find Kettle: 9.1
- Place mug: 5.4
- Coffee scoop: 15.8
- Coffee bean capacity: 7.3
- Pour hot water: 22.6

Average execution time for all tasks: 95.7 seconds (without parallel execution), 78.3 seconds (with parallel execution)

An example of parallel execution: By executing independent subtasks in parallel, such as "prepare mugs and coffee beans while the kettle boils," we were able to reduce the overall execution time.

#### Detailed analysis of multimodal feedback

A detailed analysis of the integration and effectiveness of multimodal feedback shows the following:

1. Analysis of modality integration patterns:

Contribution (weight) of each modality in different subtasks:

- Find the mug:
- Visual: 0.75
- Force sense: 0.05
- Audio: 0.20
(Vision is the primary means, with audio as a supplement for location information)

- Open the drawer:
- Visual: 0.40
- Force sense: 0.55
- Audio: 0.05
(Force sensation is the main force, with visual assistance for position adjustment)

- Scooping coffee beans:
- Visual: 0.60
- Force sense: 0.35
- Audio: 0.05
(Visual and haptic coordination)

- Pour hot water:
- Visual: 0.30
- Force sense: 0.65
- Audio: 0.05
(Force sensation is the main force, with visual assistance for position adjustment)

These weights were dynamically adjusted by an adaptive integration algorithm, which determined the optimal weights based on the reliability of each modality (sensor state, environmental conditions, etc.) and the requirements of the task.

2. Cross-modal prediction accuracy:

Accuracy of predicting one modality from another:

- Vision → Force Prediction:
- Object weight prediction: ±15% (estimated from visual features)
- Surface friction prediction: ±25% (estimated from visual texture)

- Force to visual prediction:
- Object shape prediction: 60% accuracy (estimated from tactile exploration)
- Liquid level prediction: ±12% (estimated from weight change)

- Audio to visual prediction:
- Object position prediction: 45% accuracy (estimated from sound source localization)
- Event detection: 75% accuracy (event estimation from characteristic sounds)

These predictions were generated by a cross-modal learning model (based on a variational autoencoder): the model learned the relationships between different modalities from past experience and was able to predict from one modality when the other was unavailable.

3. Performance when modality is missing:

Task completion rate when some modalities are unavailable (medium uncertainty environment):

- All modalities available: 85%
- Visual impairment (temporary):
- No supplement: 40%
- With cross-modal completion: 65%
- Force sensory deficit (temporary):
- No supplement: 55%
- With cross-modal completion: 70%
- Speech loss (temporary):
- No complement: 75%
- With cross-modal completion: 80%

These results confirmed that the cross-modal compensation mechanism significantly improved robustness against sensor failures and temporary information loss. In particular, compensation using information from haptics and audio was effective when visual information was lacking.

4. The effect of temporal integration:

Effects of intermodality integration on different time scales:

- High-speed feedback (1kHz): Improved stability of force control
- Force control error: ±0.5N (with time integration) vs ±1.2N (without)
- Touch detection delay: 5ms (with) vs 25ms (without)

- Medium feedback rate (30Hz): Improved accuracy of visual tracking
- Localization error: ±3mm (with temporal integration) vs ±8mm (without)
- Dynamic object tracking success rate: 85% (with) vs. 60% (without)

- Slow feedback (5Hz): Improved efficiency of plan updates
- Adaptation time to environmental changes: 1.2 seconds (with temporal integration) vs. 3.5 seconds (without)
- Replanning success rate: 90% (yes) vs. 75% (no)

The temporal integration algorithm was able to appropriately integrate feedback of different frequencies, achieving both fast response and long-term consistency. In particular, the combination of fast haptic feedback and medium-speed visual feedback improved the accuracy and stability of object manipulation.

Detailed analysis of RAG

The detailed analysis of the effectiveness and working mechanism of Search Augmented Generation (RAG) is as follows:

1. Precision and relevance analysis:

Search accuracy (relevance score, 0-1 scale) with different RAG configurations:

- Basic RAG (cosine similarity only):
- Average relevance score: 0.68
- Number of highly relevant documents (score > 0.8) in the top 5: 1.8

- Extended RAG (Hybrid Search):
- Average relevance score: 0.79
- Number of highly relevant documents in the top 5: 3.2

- Adaptive RAG (context aware):
- Average relevance score: 0.85
- Number of highly relevant documents in the top 5: 4.1

The relevance score was calculated based on the degree of agreement with criteria pre-evaluated by human raters.Adaptive RAG was able to retrieve more relevant documents through query expansion and leveraging contextual information.

2. Relationship between knowledge base size and RAG performance:

Task completion rate with varying knowledge base size (high uncertainty environment):

- 50 entries: 55%
- 100 entries: 62%
- 200 entries: 68%
- 400 entries: 73%
- 800 entries: 75%
- 1600 entries: 76%

These results confirm that task completion rates improve as the size of the knowledge base increases, but performance improvements slow down above about 800 entries, suggesting that not only quantity but also quality and diversity of knowledge is important.

3. Computational Efficiency of RAG:

Computation times (in milliseconds) for different RAG configurations:

- Basic RAG:
- Embedding calculation: 45ms
- Vector search: 15ms
- Context construction: 10ms
- Total: 70ms

- Extended RAG:
- Embedding calculation: 45ms
- Hybrid search: 25ms
- Re-ranking: 20ms
- Context construction: 15ms
- Total: 105ms

- Adaptive RAG:
- Query expansion: 15ms
- Embedding calculation: 45ms
- Hybrid search: 25ms
- Multi-hop search: 40ms
- Re-ranking: 20ms
- Context construction: 20ms
- Total: 165ms

These computation times were measured on a workstation equipped with an Intel Core i9-12900K CPU, 32GB RAM, and an NVIDIA RTX 3090 GPU. The embedding computation was parallelized on the GPU, and the search was performed on the CPU.

Although adaptive RAG has the highest computational cost, by introducing a caching mechanism (reusing the results of similar queries), we were able to reduce the average computation time to about 85 ms.

4. Examples of RAG's contributions:

Specific cases where RAG was particularly effective:

- Hidden object search strategies:
In the situation of "can't find the mug," RAG suggested the strategy of "searching drawers and cupboards" based on similar cases in the past. The search success rate increased from 40% to 85%.

- Improved accuracy of liquid injection:
In the task of "pouring hot water," RAG provides the appropriate pouring angle and speed based on similar liquid pouring examples. Pouring accuracy improved from ±15ml to ±8ml.

- Error recovery strategies:
In the "spill coffee beans" scenario, RAG provides the "collect with a spoon" strategy from a similar error recovery example, increasing the recovery success rate from 50% to 85%.

- Unknown object manipulation:
When a user encounters a French press for the first time, RAG provides the appropriate operation method based on examples of similar coffee makers. Task completion rate increases from 30% to 70%.

These examples demonstrate that RAG goes beyond simple information retrieval and has the ability to apply knowledge learned from past experience to new situations.

5. Knowledge transfer analysis:

The effect of knowledge transfer between similar tasks:

- "Make coffee" → "Make tea":
- No metastasis (no RAG): 60% success rate
- With metastasis (adaptive RAG): 85% success rate

- "Pick up a mug" → "Pick up a glass":
- No metastasis: 75% success rate
- With metastasis: 95% success rate

- "Open the drawer" → "Open the refrigerator":
- No metastasis: 65% success rate
- With metastasis: 90% success rate

These results confirmed that knowledge transfer through RAG significantly improved the efficiency of learning and executing new tasks, especially when the tasks involved different objects but similar basic movement patterns.

Detailed analysis of safety and efficiency

A detailed analysis of the system's safety and efficiency shows the following:

1. Effectiveness of safety constraints:

Comparison of incident rates with and without safety constraints:

- No safety constraints:
- Excessive force (>20N): Occurred in 15.3% of tasks
- High speed motion (>0.5m/s): occurred in 22.7% of task executions
- Moving outside the workspace: Occurred in 8.5% of task executions
- Collision: Occurred in 18.9% of task executions

- With safety constraints:
- Excessive force: Occurred in 0.5% of tasks (97% reduction)
- Fast operation: Occurred in 0.8% of task executions (96% reduction)
- Moving outside the workspace: Occurred in 0% of task executions (100% reduction)
- Collisions: Occurred in 3.2% of task executions (83% reduction)

It was found that the implementation of safety constraints prevented most incidents, with the remaining incidents mainly due to dynamic environmental changes (e.g., unexpected object movements).

2. Energy Efficiency Analysis:

Energy consumption for different approaches (sum of squared joint torques, relative units):

- Baseline (no RAG, single modality): 1.00 (reference)
- With RAG, single modality: 0.85 (15% reduction)
- No RAG, multimodality: 0.80 (20% reduction)
- With RAG, multimodality: 0.65 (35% reduction)

Key drivers of energy efficiency:
- Smoother trajectory planning (minimizing acceleration changes)
- Selecting the appropriate gripping force (avoiding excessive force)
- Efficient movement sequence (reducing unnecessary movements)
- Predictive control (avoiding sudden corrections by looking ahead)

The combination of multimodal feedback and RAG achieved the highest energy efficiency, demonstrating that appropriate feedback and knowledge utilization can lead to more efficient motion planning and execution.

3. Computational efficiency analysis:

Computational load by system component (CPU/GPU usage):

- Language processing components:
- CPU: 15-25%
- GPU: 60-80% (language model inference)
- Memory: 4-6GB

- Vision system:
- CPU: 10-15%
- GPU: 30-50% (for object detection and segmentation)
- Memory: 2-3GB

- Haptic module:
- CPU: 5-8%
- GPU: < 5%
- Memory: 0.5-1GB

- Multimodal integration module:
- CPU: 8-12%
- GPU: 10-20%
- Memory: 1-2GB

- Robot control system:
- CPU: 10-15%
- GPU: < 5%
- Memory: 1-2GB

Overall peak usage:
- CPU: 45-60%
- GPU: 70-90%
- Memory: 12-18GB

These measurements confirm that language model inference and visual processing are the most computationally intensive components, with potential optimization opportunities including model quantization, batch processing, and dynamic allocation of computational resources.

4. Latency Analysis:

End-to-end latency (time from input of command to start of robot movement):

- High-level instructions ("make a coffee"):
- No RAG: 3.2 seconds
- Adaptive RAG: 3.8 seconds

- Medium-level instructions ("pour coffee into a mug"):
- No RAG: 2.1 seconds
- Adaptive RAG: 2.5 seconds

- Low level instructions (detailed steps):
- No RAG: 1.3 seconds
- Adaptive RAG: 1.6 seconds

Latency breakdown by component (adaptive RAG, high level instructions):
- Voice recognition: 0.3 seconds
- Language understanding: 0.5 seconds
- RAG processing: 0.6 seconds
- Task decomposition: 1.2 seconds
- Code generation: 0.8 seconds
- Preparation time: 0.4 seconds

Although RAG introduces some latency increase, it is offset by improved task completion rate and shorter execution time. Additionally, caching mechanisms and prefetching optimizations reduce RAG latency by approximately 40%.

5. Real-time performance analysis:

Response time to environmental changes:

- Object movement detection and planning updates:
- Visual only: 450ms
- Multimodal: 320ms

- Dealing with unexpected obstacles:
- Time from detection to start of evasive action: 180ms
- Full trajectory replanning: 520ms

- Response to changes in user instructions:
- From voice recognition to plan update: 850ms
- Time until new plan begins to execute: 1200ms

These response times are sufficiently fast compared to human reaction times (approximately 250 ms for visual stimuli and approximately 170 ms for auditory stimuli), and it was confirmed that natural interaction and safe operation can be achieved.

### Conclusion and future prospects

Detailed simulation experiments using Gymnasium showed that the proposed multi-modal feedback integrated knowledge retrieval enhanced robot control system is highly effective in the following respects:

1. Ability to understand and execute high-level instructions:
The proposed system was able to understand abstract instructions such as "make a coffee" and break them down into appropriate subtasks for execution. In particular, the use of a knowledge base with RAG significantly improved the ability to understand and execute abstract instructions (from 50% without RAG to 85% with adaptive RAG).

2. The integrated effect of multimodal feedback:
The integration of vision, force, and speech achieved significantly higher task completion rates than single modalities (from up to 45% for single modality to 75% for multimodal in high uncertainty environments). In particular, the combination of vision and force was effective, and an adaptive integration approach further improved performance.

3. Ability to adapt to environmental uncertainty:
The proposed system demonstrated high adaptability to uncertainties such as randomization of object positions, variability in physical properties, and sensor noise. In particular, it achieved a task completion rate of 70% even in a highly uncertain environment, and the combination of multimodal feedback and RAG contributed greatly to improving the adaptability.

4. Efficient use of knowledge base and knowledge transfer:
The efficient use of knowledge bases through RAG improved task success rates, and knowledge transfer between similar tasks was confirmed, leading to more efficient learning and execution of new tasks (from 60-75% without transfer to 85-95% with transfer).

5. Improved safety and efficiency:
The implementation of safety constraints significantly reduced incidents such as excessive force/velocity, out-of-workspace movement, and collisions (83-100% reduction), while the combination of multi-modal feedback and RAG improved energy efficiency by 35%.

These results confirm that the proposed system is highly effective in performing complex tasks in unpredictable environments. In particular, it became clear that the combination of multimodal feedback integration and RAG significantly contributed to improving the ability to understand and adapt to the environment.

Future research topics and prospects include the following:

1. Verification with an actual robot:
The effectiveness confirmed in the simulation environment will be verified using an actual robot. Technology development is required to address the sim-to-real gap.

2. Extending to more complex tasks:
Consider application to complex tasks involving manipulation of multiple objects (e.g., cooking food, assembling objects). In particular, it will be important to evaluate long-term planning and adaptation capabilities.

3. Enhanced multimodal learning:
Developing methods to more effectively learn relationships between different modalities. In particular, efficient learning from small amounts of data and understanding causal relationships between modalities are challenges.

4. Automatic knowledge base expansion and optimization:
Development of automatic knowledge acquisition from execution experience and knowledge-based optimization methods. In particular, it is important to improve the ability to resolve conflicting knowledge and generalize knowledge.

5. Enhance natural user interactions:
Development of interaction technology to realize more natural dialogue and collaboration. In particular, understanding the user's intentions and providing appropriate feedback are key issues.

6. Distributed processing and improved computational efficiency:
Developing technology to efficiently execute computationally intensive processes, including inference of large-scale language models, in distributed systems. Low latency processing on edge devices is particularly important.

7. Consider ethical and legal considerations:
Consideration of ethical and legal frameworks regarding autonomous decisions and actions of robots. In particular, clarification of safety assurances and division of responsibilities is required.

Tackling these issues is expected to lead to the realization of robotic systems with higher levels of intelligence and adaptability. In particular, the development of robots that can collaborate with humans and execute complex tasks safely and efficiently will expand the range of possible applications in various fields, including industry, medicine, and the home.

The present invention is expected to be used in the following industrial fields:

### 1. Domestic Service Robots Home environments are unpredictable and diverse, requiring robots to have high adaptability and intelligence to perform various tasks. The system is suitable for the following domestic service robot applications:

1. Housework assistance:
- Cooking assistance: preparing ingredients, assisting with the cooking process, arranging tableware, etc.
- Cleaning: Sweeping floors, wiping surfaces, tidying things up, etc.
- Laundry: sorting laundry, loading the washing machine, folding dry clothes, etc.
- Dishwashing: collecting, washing, storing dishes, etc.

2. Support for the elderly and disabled:
- Assistance with daily living: assistance with eating, dressing, bathing, etc.
- Medication management: Providing medication at the right time, managing remaining amounts, etc.
- Mobility assistance: retrieving items, opening and closing doors, assistance with moving, etc.
- Safety monitoring: Fall detection, abnormal behavior detection, emergency reporting, etc.

3. Childcare support:
- Toy Tidying: Collect and store cluttered toys
- Educational support: interactive learning support, operation of educational toys, etc.
- Safety monitoring: detecting risky behavior, removing dangerous objects, etc.

The system's high-level instruction understanding and environmental adaptability make it well suited to deal with the diversity and unpredictability of the home environment. The integration of visual and haptic feedback also enables delicate operations (e.g., handling tableware and folding clothes). Furthermore, the voice dialogue function provides natural interaction with the user, improving ease of use.

For example, at an elderly person's home, a robot can say, "It's time to take your medicine. Shall I bring it to you?" and, after obtaining permission, prepare the medicine and water and support the elderly in taking the medicine. Also, a robot can ask, "What would you like for dinner tonight?" and help prepare food according to the elderly's request. Such natural dialogue and adaptive support are important in supporting the independent living of the elderly.

### 2. Manufacturing In the manufacturing industry, the demand for small-lot, multi-product production and flexible production lines is increasing, requiring highly adaptable robot systems. This system is suitable for the following manufacturing applications:

1. Assembly:
- Complex part assembly: Assembling parts with different shapes and arrangements
- Precision assembly: Aligning and assembling minute parts
- Flexible assembly: Adapt the assembly process to suit product variations

2. Quality Inspection:
- Visual inspection: product appearance inspection, defect detection, etc.
- Functionality testing: Product operation check, performance testing, etc.
- Dimensional measurement: Checking the dimensional accuracy of products, etc.

3. Flexible production line:
- Product changeover: Quickly switch between different products
- Process Adaptation: Adapting to changes in the manufacturing process
- Collaborative work: working with humans to complete complex tasks

The system's efficient use of the knowledge base using RAG enables it to handle a wide variety of products and manufacturing processes. The integration of visual and force feedback enables precise assembly and inspection. Furthermore, the voice dialogue function enables smooth communication with workers, improving the efficiency of collaborative work.

For example, on an automobile parts assembly line, a robot can ask a worker, "The next product is model B. Are you ready?" and prepare the necessary parts and tools. In addition, when a worker asks, "Can you tell me how to install this part?", a robot can explain the procedure or provide a demonstration. This kind of flexible response and collaborative work is important in high-mix low-volume production and complex assembly work.

### 3. Medical and Healthcare The medical and healthcare fields require high accuracy and safety, as well as the need to adapt to the different conditions of each patient. This system is suitable for the following medical and healthcare applications:

1. Surgical support:
- Instrument handover: providing surgeons with the right instruments at the right time
- Ensuring visibility: Ensuring visibility by adjusting the position of the endoscope and camera
- Tissue retention: Stable tissue retention during surgery

2. Rehabilitation support:
- Exercise support: Exercise support according to the patient's condition
- Progress monitoring: recording and analyzing rehabilitation progress
- Feedback provision: Providing real-time feedback to patients

3. Patient care:
- Meal support: Providing meals according to the patient's condition
- Medication management: Providing medicines at the right time
- Mobility assistance: getting out of bed, walking assistance, etc.

The system's high safety and adaptability make it suitable for responding to different patient situations, and the integration of visual and haptic feedback allows for delicate manipulation (e.g., instrument handover and physical interaction with the patient), while the voice dialogue function allows for natural communication with the patient, improving the quality of care.

For example, rehabilitation facilities can ask patients, "How are you feeling today?" and adjust their rehabilitation program accordingly. They can also support patients in their exercises by saying things like, "Try to push yourself a little harder," or "That's great." This kind of individualized care and encouragement is important to improve the effectiveness of rehabilitation.

### 4. Retail and Service Industries Retail and service industries require a variety of tasks, such as interacting with customers and handling products. The system is suitable for the following retail and service applications:

1. Product management:
- Product display: Proper placement and organization of products
- Inventory check: Checking stock status, etc.
- Stock replenishment: Replenishing out-of-stock items, etc.

2. Customer Service:
- Guidance: In-store guidance, product location guidance, etc.
- Information provision: Providing product information, answering questions, etc.
- Order acceptance: accepting and processing customer orders, etc.

3. Food service:
- Cooking assistance: preparing ingredients, assisting with the cooking process, etc.
- Presentation: Food presentation, decoration, etc.
- Serving: Carrying food, placing it on the table, etc.

The system's natural language understanding and creative motion generation capabilities make it suitable for interacting with customers and artistic plating. The integration of visual and haptic feedback enables delicate product handling and plating of food. Furthermore, the voice dialogue function allows natural communication with customers, improving the quality of service.

For example, in a café, a robot can greet a customer with, "Welcome. What are you looking for?" and explain the menu or make recommendations based on the customer's request. In response to the question, "How is this coffee brewed?", a robot can explain the brewing method and characteristics of the coffee. In addition, a robot can provide drinks with creative decorations such as latte art. Such personalized service and creative expression are important in increasing customer satisfaction.

### 5. Education and Research Education and research fields require flexible responses and creative problem solving. This system is suitable for the following educational and research applications:

1. Experimental support:
- Experimental preparation: preparing reagents, setting up equipment, etc.
- Data collection: recording experimental data, collecting samples, etc.
- Experiment execution: Automation of routine experimental procedures, etc.

2. Educational robots:
- Interactive learning: answering questions, explaining learning content, etc.
- Demonstrations: Demonstrations of experiments and operations, explanations of procedures, etc.
- Assessment: Evaluate learners' understanding, provide feedback, etc.

3. Research and Development Support:
- Prototyping: Rapid implementation and validation of new ideas
- Data analysis: Processing and analysis of experimental data
- Literature survey: Searching for and summarizing relevant research

The system's efficient use of the knowledge base using RAG makes it possible to handle a wide variety of experiments and research themes. In addition, the system's creative motion generation capabilities are useful for developing new experimental methods and creating educational content. Furthermore, the voice dialogue function allows for natural communication with students, improving learning outcomes.

For example, in a chemistry lab, in response to a student's question, "Please tell me the steps for this experiment," a teacher can demonstrate the steps while explaining them. In response to a student's question, "Why does this reaction occur?", a teacher can explain the mechanism of a chemical reaction. In response to a request, "Please analyze the results of this experiment," a teacher can collect data and perform statistical analysis. Such interactive learning support and practical demonstrations are important in deepening students' understanding.

### 6. Disaster Response/Dangerous Environments At disaster sites and in dangerous environments, work must be done in places where it is difficult for humans to enter. This system is suitable for the following disaster response and dangerous environment applications:

1. Disaster relief:
- Search: Search for victims, check the situation, etc.
- Rescue: Removing debris, rescuing victims, etc.
- Transporting supplies: transporting food, water, medicines, etc.

2. Disposal of dangerous materials:
- Inspection: Detecting dangerous objects, checking their condition, etc.
- Collection: safe collection and storage of hazardous materials
- Processing: Detoxifying and disposing of hazardous materials

3. Working in extreme environments:
- Deep sea operations: surveying the seabed, installing equipment, etc.
- Space operations: Space station experiments, repairs, etc.
- Nuclear facilities: inspection and repair in a radioactive environment

The system's high adaptability and autonomy make it suitable for working in unpredictable disaster sites and dangerous environments. The integration of visual and haptic feedback enables operation in complex environments and precision work. Furthermore, the voice dialogue function enables smooth communication with the remote operator, improving work efficiency and safety.

For example, at the scene of a nuclear accident, in response to a remote operator's instruction to "check the condition of this pipe," a robot can visually inspect the pipe and report the presence and extent of damage. In addition, in response to an instruction to "close this valve carefully," a robot can operate the valve with the appropriate force. Such remote operation and status reporting are important for work in environments where it is dangerous for humans to enter.

### 7. Entertainment and Arts The entertainment and arts fields require creativity and expression. This system is suitable for the following entertainment and arts applications:

1. Performance:
- Dance: Creating and executing movements in sync with music
- Drama: Emotional expression, dialogue, story progression, etc.
- Music: playing instruments, singing, etc.

2. Creative activities:
- Drawing: painting, illustration, graphic design, etc.
- Sculpture: 3D modeling, 3D art, etc.
- Crafts: pottery, weaving, woodworking, etc.

3. Interactive Art:
- Audience participation works: works that respond to the movements and voices of the audience
- Media art: artistic expression using digital technology
- Environmental art: an artistic experience that utilizes the entire space

The system's creative motion generation and natural language understanding capabilities make it well suited for artistic expression and audience interaction. The integration of visual and haptic feedback enables delicate artistic manipulation (e.g., painting, sculpting). Furthermore, the voice dialogue function allows natural communication with the audience, enhancing the immersiveness of the performance.

For example, an interactive art exhibit can ask the audience, "What is your favorite color?" and then create an impromptu painting based on the answer. Or, in response to the question, "How are you feeling?", music and movement can be generated to match the audience's emotions. Such dialogue with the audience and impromptu creation are important in making the art experience more personalized and immersive.

In these industrial fields, this system is expected to enable the execution of complex tasks that were difficult for conventional robot systems, and contribute to improving productivity, ensuring safety, and creating new services. In particular, the ability to adapt in unpredictable environments and understand high-level abstract instructions can create value in various application scenarios. In addition, the integration and utilization of multimodal feedback realizes richer environmental understanding and adaptive behavior generation, enabling natural interaction with humans. This allows robots to function not just as tools, but as collaborative partners for humans.

### 8. Agriculture and food industry In the agriculture and food industry, it is necessary to respond to changes in environmental conditions and individual differences in crops and ingredients. This system is suitable for the following agriculture and food industry applications:

1. Precision agriculture:
- Crop management: monitoring crop status, watering, fertilizing, etc.
- Harvesting: determining ripeness, harvesting with the right force, sorting, etc.
- Environmental control: Optimization of temperature, humidity, light, etc.

2. Food processing:
- Raw material processing: washing, peeling, cutting, separating, etc.
- Cooking: mixing, heating, cooling, fermentation control, etc.
- Packaging: filling the right amount, sealing, labeling, etc.

3. Quality control:
- Visual inspection: shape, color, flaw detection, etc.
- Physical property measurements: hardness, viscosity, moisture content, etc.
- Ingredient analysis: detection of nutrients, additives, foreign substances, etc.

The system's integration of visual and force feedback enables appropriate processing according to the state of the crop or food ingredient. In addition, efficient use of the knowledge base using RAG allows it to handle a wide variety of crops and ingredients. Furthermore, its ability to adapt to environmental changes and uncertainties allows stable operation even in outdoor environments and under fluctuating conditions.

For example, in a tomato farm, in response to the instruction, "Check the ripeness of these tomatoes and select the ones that can be harvested," the robot can judge the ripeness from visual information and harvest with the appropriate force. In addition, in response to the instruction, "Judge whether this plot needs watering," the robot can make a decision taking into account the condition of the soil and weather conditions. Such precise management and judgment contribute to improving agricultural productivity and quality.

In a food processing factory, when an instruction is given to "cut this vegetable into uniform pieces," the robot can recognize the shape of the vegetable from visual information and adjust the cutting position and force appropriately. In addition, when an instruction is given to "check the viscosity of this sauce and adjust it as necessary," the robot can estimate the viscosity from haptic feedback and adjust it accordingly. Such precise processing and quality control are important to ensure the consistency and safety of food.

### 9. Logistics and Warehouse Management In the logistics and warehouse management field, products of various shapes and weights are handled and efficient storage and shipping are required. This system is suitable for the following logistics and warehouse management applications:

1. Product picking:
- Product identification: Product identification by barcode, QR code, and appearance
- Adaptive grip: Adjust gripping method according to product shape, weight, and hardness
- Packaging: Proper product placement, inserting cushioning material, sealing the box, etc.

2. Inventory Management:
- Inventory: Automatic counting, location and condition check of products
- Product placement: Planning and implementing efficient storage layouts
- Inventory replenishment: Detecting out-of-stocks and generating replenishment orders

3. Logistics optimization:
- Route planning: Plan and execute efficient travel routes
- Loading optimization: Efficient product placement on trucks and shipping boxes
- Delivery preparation: sorting and preparing products according to delivery order

The system's integration of visual and haptic feedback allows it to handle a wide variety of products appropriately, and its efficient use of knowledge base using RAG allows it to adapt to new products and packaging methods. Furthermore, its ability to adapt to environmental changes and uncertainties allows it to handle busy warehouse environments and fluctuating order patterns.

For example, in an e-commerce warehouse, in response to an instruction to "Pick and pack the items on this order list," the system can identify each item and pick and pack it in the appropriate way. In addition, in response to an instruction to "Check the inventory status on this shelf," the system can scan the items and report the number of items in stock and their location. Such efficient picking and inventory management contributes to reducing logistics costs and improving customer satisfaction.

### 10. Smart City and Public Services In the field of smart cities and public services, it is necessary to respond to the diverse needs of citizens and maintain a safe and efficient urban environment. This system is suitable for the following smart city and public service applications:

1. Public space management:
- Cleaning: cleaning roads, parks, public facilities, etc.
- Inspection: Inspection of infrastructure and public facilities
- Repairs: small repair work, maintenance

2. Citizen Services:
- Guidance: Tourist information, facility information, route information, etc.
- Information provision: event information, traffic information, weather information, etc.
- Support: Mobility support for the elderly and disabled, luggage transport, etc.

3. Safety management:
- Surveillance: Detecting abnormal behavior, recognizing dangerous situations
- Reporting: Reporting emergencies and contacting relevant authorities
- Initial response: initial firefighting, evacuation guidance, first aid, etc.

The system's high level command understanding and environmental adaptability make it suitable for responding to diverse public spaces and citizen needs. The integration of visual and haptic feedback also enables delicate tasks (e.g., sorting garbage, inspecting equipment). Furthermore, the voice dialogue function enables natural communication with citizens, improving the quality of services.

For example, in a public facility, when a visitor requests, "Please show me around this building," the system can provide an overview of the facility and guide them to their destination. In addition, when a manager instructs, "Please check the condition of this equipment," the system can visually inspect the equipment and report whether there are any abnormalities. Such interactive guidance and inspections contribute to improving the efficiency and quality of public services.

In parks and squares, when you say, "Please clean up this area," you can identify the trash and collect it in the appropriate way. Similarly, when you say, "Please check the water pressure in this fountain," you can observe the condition of the fountain and report any abnormalities. Such daily management and inspections are important for maintaining the beauty and safety of public spaces.

### 11. Space Exploration and Extreme Environments Space exploration and operations in extreme environments require high levels of autonomy and adaptability. The system is suitable for space exploration and extreme environment applications such as:

1. Space Exploration:
- Planetary surface exploration: topographical survey, sample collection, and experimentation
- Space Station Operations: Equipment installation, repair, maintenance
- Space construction: Assembling structures and building resource utilization facilities

2. Deep sea exploration:
- Ocean bottom survey: topographical mapping, ecosystem observation, resource exploration
- Subsea operations: cable laying, equipment installation, sampling
- Subsea structure inspection: Oil platforms, bridges, pipelines

3. Polar Exploration:
- Ice sheet survey: ice layer structure analysis, core collection, meteorological observation
- Polar base support: material transportation, equipment maintenance, research support
- Environmental monitoring: ecosystem observation, pollutant detection, climate change data collection

The system's high autonomy and adaptability make it suitable for working in environments with limited communication delays and restrictions. The integration of visual and haptic feedback enables safe operation in unknown environments. Furthermore, the efficient use of knowledge base with RAG allows the system to respond to unexpected situations.

For example, in a Mars exploration mission, in response to an instruction from Earth to "collect a sample of this rock," the robot can collect the sample in an appropriate manner according to the hardness and shape of the rock. In response to an instruction to "investigate this terrain and find a safe route," the robot can analyze the surrounding terrain and move safely while avoiding obstacles. Such autonomous judgment and adaptation are essential for exploration in remote environments with communication delays.

In the space station, when an astronaut requests, "Please help me repair this device," a robot can prepare the necessary tools and assist with the repair while explaining the repair procedure. In addition, when an astronaut requests, "Please set up this experimental device," a robot can properly position and connect each part of the device. Such precise work and assistance is important in the space environment with limited resources and strict safety requirements.

### 12. Educational and Training Simulation In the field of educational and training simulation, it is necessary to reproduce realistic environments and situations to provide an effective learning experience. This system is suitable for the following educational and training simulation applications:

1. Medical Training:
- Surgery simulation: surgical procedures, technique practice
- Emergency response training: emergency medical procedures, triage practice
- Patient care training: Acquire skills in diagnosis, nursing, and rehabilitation

2. Technical training:
- Industrial technology: training in machine operation, assembly, repair, inspection
- Construction technology: learning heavy machinery operation, surveying, and construction techniques
- Science experiments: Chemistry, physics, and biology experiments

3. Disaster response training:
- Evacuation guidance: Training in evacuation plans and guidance techniques in the event of a disaster
- Rescue operations: Searching for victims, rescuing them, and administering first aid
- Crisis management: chain of command, information gathering, decision-making training

The system's integration of visual and haptic feedback allows it to provide a realistic tactile and visual experience, its natural language understanding capability allows interactive instruction with learners, and its ability to adapt to environmental changes and uncertainties allows it to handle a variety of scenarios and levels of difficulty.

For example, in medical education, in response to the instruction "Please examine this patient," a virtual patient's symptoms can be represented and the student's questions can be answered. In response to the request "Please demonstrate this surgical procedure," a virtual machine can demonstrate the procedure while explaining each step, providing feedback as the student performs it. Such interactive simulations and immediate feedback are effective in acquiring medical skills.

In disaster response training, for a scenario such as "A fire has broken out in this building. Please guide evacuation," changes in the situation, such as the spread of the fire and smoke filling the building, can be reproduced, and the situation can be changed according to the trainee's judgment and actions. In addition, in a scenario such as "There is a victim under this rubble. Please rescue him," it is possible to train appropriate rescue methods according to the stability of the rubble and the condition of the victim. Such dynamic simulations and situation responses contribute to improving practical disaster response capabilities.

Claims (3)

A multimodal feedback integrated knowledge retrieval enhanced robot control system, comprising a language processing component, a vision system, a force sensing module, a speech recognition and synthesis module, a robot control system, a multimodal integration module, and a curated knowledge base, wherein the language processing component dynamically selects and adapts relevant examples from the curated knowledge base using retrieval enhanced generation (RAG) techniques. The multimodal feedback integrated knowledge retrieval enhanced robot control system of claim 1, characterized in that the vision system uses a depth camera to generate a three-dimensional representation of the environment, the force module uses a multi-axis force sensor to measure the forces experienced by the robot's end effector, the multimodal integration module appropriately weights and integrates information from each modality, and the robot control system incorporates safety constraints such as maximum speed and force limits and workspace boundaries. A multimodal feedback integrated knowledge retrieval enhanced robot control method, comprising the steps of: a language processing component processes a user's query and environmental data to decompose a complex task; selecting relevant examples from a knowledge base using a search enhancement generation technique; generating executable code; a vision system generating a three-dimensional representation of the environment; a haptic module measuring the force of an end effector; a speech recognition and synthesis module interacting with a user; a multimodal integration module integrating information of different modalities; and a robot control system integrating feedback to control the robot's operation.