Enhancing the LLM-Based Robot Manipulation Through Human-Robot Collaboration

Haokun Liu, Yaonan Zhu, Kenji Kato, Atsushi Tsukahara, Izumi Kondo, Tadayoshi Aoyama, Yasuhisa Hasegawa·June 20, 2024

Summary

The paper proposes a novel approach to enhance LLM-based robot manipulation by integrating human-robot collaboration (HRC). It uses GPT-4 for task decomposition and a YOLO-based perception system for environment awareness. A key feature is the combination of teleoperation with Dynamic Movement Primitives (DMPs) for learning from human guidance. Experiments with the Toyota Human Support Robot demonstrate improved performance in complex tasks, particularly in tasks requiring environment-aware planning. The system leverages both pre-programmed and learned motion sequences, showing high executability rates. However, the study also highlights the need for further research to address limitations in handling complex trajectories and incorporating additional sensory inputs for enhanced autonomy in real-world scenarios.

Key findings

4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of enhancing the performance of Large Language Model (LLM)-based robot manipulation through Human-Robot Collaboration (HRC) by integrating language models, robots, and the environment more effectively . This problem is not entirely new, as previous research has focused on basic and straightforward task planning with LLMs, overlooking more complex, long-horizon tasks that require high-level reasoning and motion planning . The paper proposes a novel approach to overcome these limitations and improve the autonomy and responsiveness of LLM-based robots in executing intricate tasks .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that enhancing Large Language Model (LLM)-based robot manipulation through Human-Robot Collaboration (HRC) can improve the performance of autonomous manipulation tasks . The study focuses on integrating environmental perception to enable the robot to autonomously interpret and act upon user instructions in real-world scenarios, emphasizing the synergy between language models, robots, and the environment . The research explores the use of prompted GPT-4 language models to decompose high-level language commands into motion sequences, incorporating a YOLO-based perception algorithm to provide visual cues for feasible motion planning within specific environments . Additionally, the paper proposes an HRC method that combines teleoperation and Dynamic Movement Primitives (DMP) to facilitate learning from human guidance, ultimately enhancing the robot's ability to perform complex tasks efficiently .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several innovative ideas, methods, and models to enhance LLM-based robot manipulation through Human-Robot Collaboration (HRC) . Here are the key contributions outlined in the paper:

  1. GPT4-based LLM System: The paper introduces a GPT4-based LLM system to facilitate task planning for complex, long-horizon tasks. This system allows the LLM to select motion functions from a motion library based on natural language commands. These functions are then integrated with environmental information perceived through a YOLOv5-based perception module, enabling the autonomous execution of a wide range of tasks .

  2. Hierarchical Planning Framework: The proposed LLM system adopts a hierarchical planning framework by utilizing the prompt function of GPT4. This framework enables the LLM to dissect complex tasks into sub-tasks, further break down the sub-tasks into several motion functions, and execute each motion function sequentially .

  3. Teleoperation-based HRC Framework: The paper introduces a teleoperation-based HRC framework for motion demonstration. By integrating with Dynamic Movement Primitives (DMP), this framework allows the LLM-based robot to learn from human demonstrations, thereby enhancing its motion capabilities. This framework significantly improves the LLM-based system's capabilities in executing complex tasks that require intricate trajectory planning and reasoning over environments .

  4. Motion Libraries: The system utilizes two primary libraries for motion execution: the basic library and the DMP library. The basic library includes pre-programmed motion functions selected by the LLM based on task requirements, while the DMP library stores updated motion function sequences for sub-tasks. These sequences are updated through the user interface leveraging teleoperation .

  5. Experimental Results: The experimental results of the proposed method show an average success rate of 79.5%, with high executability and feasibility across various tasks. These results demonstrate the system's robustness in translating language commands into robot motions and integrating operator instructions to accomplish challenging tasks .

Overall, the paper presents a comprehensive framework that combines advanced technologies like LLMs, environmental perception, teleoperation, and DMP to enhance the performance of LLM-based robot manipulation through human collaboration, addressing the challenges of complex task planning and execution in real-world scenarios . The proposed LLM-based robot manipulation framework combined with Human-Robot Collaboration (HRC) offers several key characteristics and advantages compared to previous methods outlined in the paper :

  1. Integration of Teleoperation System: Unlike simple LLM-based autonomy, the proposed framework integrates a teleoperation system, allowing user input during autonomous processes. This feature ensures human guidance for complex tasks, enhancing the system's adaptability and responsiveness .

  2. Hierarchical Planning Framework: The system adopts a hierarchical planning framework by utilizing the prompt function of GPT4. This enables the LLM to break down complex tasks into sub-tasks and execute them sequentially, enhancing task planning efficiency .

  3. Teleoperation-based HRC Framework: The incorporation of a teleoperation-based HRC framework allows the LLM-based robot to learn from human demonstrations, thereby augmenting its motion capabilities. This approach significantly enhances the system's ability to execute complex tasks that require intricate trajectory planning and reasoning over environments .

  4. Motion Libraries: The system utilizes two primary libraries for motion execution: the basic library and the DMP library. The basic library includes pre-programmed motion functions selected based on task requirements, while the DMP library stores updated motion function sequences for sub-tasks. This approach promotes task-specific autonomy and enriches the system's learning efficiency .

  5. Enhanced Task Execution: The proposed HRC framework significantly enhances the capabilities of the LLM-based system in executing complex tasks. By combining teleoperation and DMP, the system can efficiently accomplish tasks that require sophisticated trajectory planning and reasoning in real-world scenarios, showcasing improved performance and adaptability .

Overall, the characteristics and advantages of the proposed framework lie in its seamless integration of teleoperation, hierarchical planning, and motion libraries, enabling the LLM-based robot to efficiently execute complex tasks with human guidance and enhanced learning capabilities, addressing the limitations of previous methods and advancing the field of autonomous robot manipulation through innovative Human-Robot Collaboration .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of enhancing LLM-based robot manipulation through human-robot collaboration. Noteworthy researchers in this area include Yaonan Zhu, Tadayoshi Aoyama, Yasuhisa Hasegawa, Haokun Liu, Kenji Kato, Atsushi Tsukahara, and Izumi Kondo . These researchers have contributed to the development of innovative approaches to improve the performance of LLM-based autonomous manipulation.

The key to the solution proposed in the paper involves a prompted GPT-4 language model to decompose high-level language commands into sequences of motions that can be executed by the robot. Additionally, the system utilizes a YOLO-based perception algorithm to provide visual cues to the LLM, aiding in planning feasible motions within the specific environment. Furthermore, an HRC method is introduced by combining teleoperation and Dynamic Movement Primitives (DMP), allowing the LLM-based robot to learn from human guidance . This collaborative framework enables users to interact proactively with the robot, instructing it seamlessly through teleoperation, and saving new skills for future tasks.


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of the Human-Robot Collaboration (HRC) framework based on the Learned Latent Model (LLM) on the Human Support Robot (HSR) from Toyota . The experiments were conducted in a well-lit kitchen environment where the robot performed various domestic tasks based on natural language commands provided by users . The evaluation included both routine zero-shot tasks and more complex one-shot tasks to assess the system's capabilities . The experiments aimed to test the system's performance in executing tasks with different levels of complexity, from basic tasks like Put&Stack to more specialized tasks like Warm up apple and Roast apple (HRC) .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is indicated in Table I of the document, which includes tasks such as Put&Stack, Open microwave, Open oven (HRC), Open cabinet (HRC), Clean table, Warm up apple, and Roast apple (HRC) . The code used in the study is not explicitly mentioned as open source in the provided context .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study conducted real-world experiments using the Toyota Human Support Robot for manipulation tasks, demonstrating the effectiveness of the proposed approach in enhancing the performance of LLM-based autonomous manipulation through Human-Robot Collaboration (HRC) . The outcomes of the experiments indicated that tasks requiring complex trajectory planning and reasoning over environments could be efficiently accomplished through the incorporation of human demonstrations .

The results of the experiments, as shown in the tables provided in the paper, highlight the performance metrics such as Executability, Feasibility, and Success rates for various tasks . These metrics quantitatively assess the robot's ability to execute tasks successfully, plan feasible motions, and achieve desired outcomes. The high success rates achieved in tasks like "Put&Stack" and "Open oven (HRC)" demonstrate the effectiveness of the proposed approach in improving task execution .

Furthermore, the study identified specific challenges and issues encountered during the experiments, such as inaccuracies in the DMP function calls and errors in environmental perception . By addressing these challenges and analyzing the causes of reduced success rates in certain tasks, the study provides valuable insights into the limitations and areas for improvement in LLM-based robot manipulation through HRC.

In conclusion, the experiments and results presented in the paper offer comprehensive and detailed analysis supporting the scientific hypotheses under investigation. The study's methodology, results, and discussions contribute to advancing the understanding of how Human-Robot Collaboration can enhance the performance of LLM-based robot manipulation, providing valuable insights for future research in this field.


What are the contributions of this paper?

The contributions of the paper "Enhancing the LLM-Based Robot Manipulation Through Human-Robot Collaboration" include:

  1. Development of a GPT4-based LLM system for task planning, selecting motion functions based on natural language commands, integrating environmental information through a YOLOv5-based perception module, and enabling autonomous execution of various tasks .
  2. Adoption of a hierarchical planning framework using the prompt function of GPT4 to break down complex tasks into sub-tasks, further decompose sub-tasks into motion functions, and execute each function sequentially .
  3. Introduction of a teleoperation-based Human-Robot Collaboration (HRC) framework for motion demonstration, integrating with Dynamic Movement Primitives (DMP) to enable the LLM-based robot to learn from human demonstrations and enhance its motion capabilities .
  4. Significant enhancement of the LLM-based system's capabilities in executing complex tasks, particularly those requiring intricate trajectory planning and reasoning over environments, through the incorporation of human demonstrations .

What work can be continued in depth?

To further enhance the performance of LLM-based robots, future research can focus on integrating LIDAR-derived point clouds and tactile sensing technologies. This integration aims to improve the robot's performance in real-world environments by reducing its reliance on visual inputs and enhancing its ability to perceive and interact with the surroundings . Additionally, extending the input channels and capabilities of LLM-based robots is essential for advancing their autonomy and effectiveness in handling complex tasks .

Tables

2

Introduction
Background
Evolution of LLMs in robotics
Importance of human-robot collaboration
Objective
To propose a novel approach for improved robot manipulation
Integrate GPT-4 and YOLO for task decomposition and perception
Focus on teleoperation with DMPs for learning from human guidance
Methodology
Task Decomposition with GPT-4
Utilizing GPT-4 for task understanding and decomposition
Breakdown of complex tasks into manageable steps
Environment Awareness with YOLO
YOLO-based perception system for real-time object detection
Role in environment planning and obstacle avoidance
Teleoperation with Dynamic Movement Primitives (DMPs)
Human guidance integration through teleoperation
Learning from human demonstrations for skill acquisition
Experimentation
Toyota Human Support Robot (THSR)
Implementation on the THSR for complex task performance
Performance Evaluation
Improved performance in environment-aware tasks
Executability rates of pre-programmed and learned motion sequences
Limitations and Future Research
Complex trajectory handling challenges
Incorporating additional sensory inputs for autonomy
Real-world scenarios and scalability
Conclusion
Summary of key findings and contributions
Potential impact on future LLM-driven robot manipulation
Recommendations for future research directions
Basic info
papers
human-computer interaction
robotics
artificial intelligence
Advanced features
Insights
Which robot is used for experimental demonstration in the paper?
What are the key findings regarding performance improvement in complex tasks with the proposed method?
How does the proposed approach integrate human-robot collaboration in the system?
What technology does the paper utilize for task decomposition in robot manipulation?

Enhancing the LLM-Based Robot Manipulation Through Human-Robot Collaboration

Haokun Liu, Yaonan Zhu, Kenji Kato, Atsushi Tsukahara, Izumi Kondo, Tadayoshi Aoyama, Yasuhisa Hasegawa·June 20, 2024

Summary

The paper proposes a novel approach to enhance LLM-based robot manipulation by integrating human-robot collaboration (HRC). It uses GPT-4 for task decomposition and a YOLO-based perception system for environment awareness. A key feature is the combination of teleoperation with Dynamic Movement Primitives (DMPs) for learning from human guidance. Experiments with the Toyota Human Support Robot demonstrate improved performance in complex tasks, particularly in tasks requiring environment-aware planning. The system leverages both pre-programmed and learned motion sequences, showing high executability rates. However, the study also highlights the need for further research to address limitations in handling complex trajectories and incorporating additional sensory inputs for enhanced autonomy in real-world scenarios.
Mind map
Executability rates of pre-programmed and learned motion sequences
Improved performance in environment-aware tasks
Implementation on the THSR for complex task performance
Real-world scenarios and scalability
Incorporating additional sensory inputs for autonomy
Complex trajectory handling challenges
Performance Evaluation
Toyota Human Support Robot (THSR)
Learning from human demonstrations for skill acquisition
Human guidance integration through teleoperation
Role in environment planning and obstacle avoidance
YOLO-based perception system for real-time object detection
Breakdown of complex tasks into manageable steps
Utilizing GPT-4 for task understanding and decomposition
Focus on teleoperation with DMPs for learning from human guidance
Integrate GPT-4 and YOLO for task decomposition and perception
To propose a novel approach for improved robot manipulation
Importance of human-robot collaboration
Evolution of LLMs in robotics
Recommendations for future research directions
Potential impact on future LLM-driven robot manipulation
Summary of key findings and contributions
Limitations and Future Research
Experimentation
Teleoperation with Dynamic Movement Primitives (DMPs)
Environment Awareness with YOLO
Task Decomposition with GPT-4
Objective
Background
Conclusion
Methodology
Introduction
Outline
Introduction
Background
Evolution of LLMs in robotics
Importance of human-robot collaboration
Objective
To propose a novel approach for improved robot manipulation
Integrate GPT-4 and YOLO for task decomposition and perception
Focus on teleoperation with DMPs for learning from human guidance
Methodology
Task Decomposition with GPT-4
Utilizing GPT-4 for task understanding and decomposition
Breakdown of complex tasks into manageable steps
Environment Awareness with YOLO
YOLO-based perception system for real-time object detection
Role in environment planning and obstacle avoidance
Teleoperation with Dynamic Movement Primitives (DMPs)
Human guidance integration through teleoperation
Learning from human demonstrations for skill acquisition
Experimentation
Toyota Human Support Robot (THSR)
Implementation on the THSR for complex task performance
Performance Evaluation
Improved performance in environment-aware tasks
Executability rates of pre-programmed and learned motion sequences
Limitations and Future Research
Complex trajectory handling challenges
Incorporating additional sensory inputs for autonomy
Real-world scenarios and scalability
Conclusion
Summary of key findings and contributions
Potential impact on future LLM-driven robot manipulation
Recommendations for future research directions
Key findings
4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of enhancing the performance of Large Language Model (LLM)-based robot manipulation through Human-Robot Collaboration (HRC) by integrating language models, robots, and the environment more effectively . This problem is not entirely new, as previous research has focused on basic and straightforward task planning with LLMs, overlooking more complex, long-horizon tasks that require high-level reasoning and motion planning . The paper proposes a novel approach to overcome these limitations and improve the autonomy and responsiveness of LLM-based robots in executing intricate tasks .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that enhancing Large Language Model (LLM)-based robot manipulation through Human-Robot Collaboration (HRC) can improve the performance of autonomous manipulation tasks . The study focuses on integrating environmental perception to enable the robot to autonomously interpret and act upon user instructions in real-world scenarios, emphasizing the synergy between language models, robots, and the environment . The research explores the use of prompted GPT-4 language models to decompose high-level language commands into motion sequences, incorporating a YOLO-based perception algorithm to provide visual cues for feasible motion planning within specific environments . Additionally, the paper proposes an HRC method that combines teleoperation and Dynamic Movement Primitives (DMP) to facilitate learning from human guidance, ultimately enhancing the robot's ability to perform complex tasks efficiently .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several innovative ideas, methods, and models to enhance LLM-based robot manipulation through Human-Robot Collaboration (HRC) . Here are the key contributions outlined in the paper:

  1. GPT4-based LLM System: The paper introduces a GPT4-based LLM system to facilitate task planning for complex, long-horizon tasks. This system allows the LLM to select motion functions from a motion library based on natural language commands. These functions are then integrated with environmental information perceived through a YOLOv5-based perception module, enabling the autonomous execution of a wide range of tasks .

  2. Hierarchical Planning Framework: The proposed LLM system adopts a hierarchical planning framework by utilizing the prompt function of GPT4. This framework enables the LLM to dissect complex tasks into sub-tasks, further break down the sub-tasks into several motion functions, and execute each motion function sequentially .

  3. Teleoperation-based HRC Framework: The paper introduces a teleoperation-based HRC framework for motion demonstration. By integrating with Dynamic Movement Primitives (DMP), this framework allows the LLM-based robot to learn from human demonstrations, thereby enhancing its motion capabilities. This framework significantly improves the LLM-based system's capabilities in executing complex tasks that require intricate trajectory planning and reasoning over environments .

  4. Motion Libraries: The system utilizes two primary libraries for motion execution: the basic library and the DMP library. The basic library includes pre-programmed motion functions selected by the LLM based on task requirements, while the DMP library stores updated motion function sequences for sub-tasks. These sequences are updated through the user interface leveraging teleoperation .

  5. Experimental Results: The experimental results of the proposed method show an average success rate of 79.5%, with high executability and feasibility across various tasks. These results demonstrate the system's robustness in translating language commands into robot motions and integrating operator instructions to accomplish challenging tasks .

Overall, the paper presents a comprehensive framework that combines advanced technologies like LLMs, environmental perception, teleoperation, and DMP to enhance the performance of LLM-based robot manipulation through human collaboration, addressing the challenges of complex task planning and execution in real-world scenarios . The proposed LLM-based robot manipulation framework combined with Human-Robot Collaboration (HRC) offers several key characteristics and advantages compared to previous methods outlined in the paper :

  1. Integration of Teleoperation System: Unlike simple LLM-based autonomy, the proposed framework integrates a teleoperation system, allowing user input during autonomous processes. This feature ensures human guidance for complex tasks, enhancing the system's adaptability and responsiveness .

  2. Hierarchical Planning Framework: The system adopts a hierarchical planning framework by utilizing the prompt function of GPT4. This enables the LLM to break down complex tasks into sub-tasks and execute them sequentially, enhancing task planning efficiency .

  3. Teleoperation-based HRC Framework: The incorporation of a teleoperation-based HRC framework allows the LLM-based robot to learn from human demonstrations, thereby augmenting its motion capabilities. This approach significantly enhances the system's ability to execute complex tasks that require intricate trajectory planning and reasoning over environments .

  4. Motion Libraries: The system utilizes two primary libraries for motion execution: the basic library and the DMP library. The basic library includes pre-programmed motion functions selected based on task requirements, while the DMP library stores updated motion function sequences for sub-tasks. This approach promotes task-specific autonomy and enriches the system's learning efficiency .

  5. Enhanced Task Execution: The proposed HRC framework significantly enhances the capabilities of the LLM-based system in executing complex tasks. By combining teleoperation and DMP, the system can efficiently accomplish tasks that require sophisticated trajectory planning and reasoning in real-world scenarios, showcasing improved performance and adaptability .

Overall, the characteristics and advantages of the proposed framework lie in its seamless integration of teleoperation, hierarchical planning, and motion libraries, enabling the LLM-based robot to efficiently execute complex tasks with human guidance and enhanced learning capabilities, addressing the limitations of previous methods and advancing the field of autonomous robot manipulation through innovative Human-Robot Collaboration .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of enhancing LLM-based robot manipulation through human-robot collaboration. Noteworthy researchers in this area include Yaonan Zhu, Tadayoshi Aoyama, Yasuhisa Hasegawa, Haokun Liu, Kenji Kato, Atsushi Tsukahara, and Izumi Kondo . These researchers have contributed to the development of innovative approaches to improve the performance of LLM-based autonomous manipulation.

The key to the solution proposed in the paper involves a prompted GPT-4 language model to decompose high-level language commands into sequences of motions that can be executed by the robot. Additionally, the system utilizes a YOLO-based perception algorithm to provide visual cues to the LLM, aiding in planning feasible motions within the specific environment. Furthermore, an HRC method is introduced by combining teleoperation and Dynamic Movement Primitives (DMP), allowing the LLM-based robot to learn from human guidance . This collaborative framework enables users to interact proactively with the robot, instructing it seamlessly through teleoperation, and saving new skills for future tasks.


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of the Human-Robot Collaboration (HRC) framework based on the Learned Latent Model (LLM) on the Human Support Robot (HSR) from Toyota . The experiments were conducted in a well-lit kitchen environment where the robot performed various domestic tasks based on natural language commands provided by users . The evaluation included both routine zero-shot tasks and more complex one-shot tasks to assess the system's capabilities . The experiments aimed to test the system's performance in executing tasks with different levels of complexity, from basic tasks like Put&Stack to more specialized tasks like Warm up apple and Roast apple (HRC) .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is indicated in Table I of the document, which includes tasks such as Put&Stack, Open microwave, Open oven (HRC), Open cabinet (HRC), Clean table, Warm up apple, and Roast apple (HRC) . The code used in the study is not explicitly mentioned as open source in the provided context .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study conducted real-world experiments using the Toyota Human Support Robot for manipulation tasks, demonstrating the effectiveness of the proposed approach in enhancing the performance of LLM-based autonomous manipulation through Human-Robot Collaboration (HRC) . The outcomes of the experiments indicated that tasks requiring complex trajectory planning and reasoning over environments could be efficiently accomplished through the incorporation of human demonstrations .

The results of the experiments, as shown in the tables provided in the paper, highlight the performance metrics such as Executability, Feasibility, and Success rates for various tasks . These metrics quantitatively assess the robot's ability to execute tasks successfully, plan feasible motions, and achieve desired outcomes. The high success rates achieved in tasks like "Put&Stack" and "Open oven (HRC)" demonstrate the effectiveness of the proposed approach in improving task execution .

Furthermore, the study identified specific challenges and issues encountered during the experiments, such as inaccuracies in the DMP function calls and errors in environmental perception . By addressing these challenges and analyzing the causes of reduced success rates in certain tasks, the study provides valuable insights into the limitations and areas for improvement in LLM-based robot manipulation through HRC.

In conclusion, the experiments and results presented in the paper offer comprehensive and detailed analysis supporting the scientific hypotheses under investigation. The study's methodology, results, and discussions contribute to advancing the understanding of how Human-Robot Collaboration can enhance the performance of LLM-based robot manipulation, providing valuable insights for future research in this field.


What are the contributions of this paper?

The contributions of the paper "Enhancing the LLM-Based Robot Manipulation Through Human-Robot Collaboration" include:

  1. Development of a GPT4-based LLM system for task planning, selecting motion functions based on natural language commands, integrating environmental information through a YOLOv5-based perception module, and enabling autonomous execution of various tasks .
  2. Adoption of a hierarchical planning framework using the prompt function of GPT4 to break down complex tasks into sub-tasks, further decompose sub-tasks into motion functions, and execute each function sequentially .
  3. Introduction of a teleoperation-based Human-Robot Collaboration (HRC) framework for motion demonstration, integrating with Dynamic Movement Primitives (DMP) to enable the LLM-based robot to learn from human demonstrations and enhance its motion capabilities .
  4. Significant enhancement of the LLM-based system's capabilities in executing complex tasks, particularly those requiring intricate trajectory planning and reasoning over environments, through the incorporation of human demonstrations .

What work can be continued in depth?

To further enhance the performance of LLM-based robots, future research can focus on integrating LIDAR-derived point clouds and tactile sensing technologies. This integration aims to improve the robot's performance in real-world environments by reducing its reliance on visual inputs and enhancing its ability to perceive and interact with the surroundings . Additionally, extending the input channels and capabilities of LLM-based robots is essential for advancing their autonomy and effectiveness in handling complex tasks .

Tables
2
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