Leveraging large language models for comprehensive locomotion control in humanoid robots design
This paper investigates the utilization of large language models (LLMs) for the comprehensive control of humanoid robot locomotion. Traditional reinforcement learning (RL) approaches for robot locomotion are resource-intensive and rely heavily on manually designed reward functions. To address these...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Biomimetic Intelligence and Robotics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667379724000457 |
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| author | Shilong Sun Chiyao Li Zida Zhao Haodong Huang Wenfu Xu |
| author_facet | Shilong Sun Chiyao Li Zida Zhao Haodong Huang Wenfu Xu |
| author_sort | Shilong Sun |
| collection | DOAJ |
| description | This paper investigates the utilization of large language models (LLMs) for the comprehensive control of humanoid robot locomotion. Traditional reinforcement learning (RL) approaches for robot locomotion are resource-intensive and rely heavily on manually designed reward functions. To address these challenges, we propose a method that employs LLMs as the primary designer to handle key aspects of locomotion control, such as trajectory planning, inverse kinematics solving, and reward function design. By using user-provided prompts, LLMs generate and optimize code, reducing the need for manual intervention. Our approach was validated through simulations in Unity, demonstrating that LLMs can achieve human-level performance in humanoid robot control. The results indicate that LLMs can simplify and enhance the development of advanced locomotion control systems for humanoid robots. |
| format | Article |
| id | doaj-art-676ecb01bc4e47e480a50783ffd991b6 |
| institution | DOAJ |
| issn | 2667-3797 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biomimetic Intelligence and Robotics |
| spelling | doaj-art-676ecb01bc4e47e480a50783ffd991b62025-08-20T02:40:08ZengElsevierBiomimetic Intelligence and Robotics2667-37972024-12-014410018710.1016/j.birob.2024.100187Leveraging large language models for comprehensive locomotion control in humanoid robots designShilong Sun0Chiyao Li1Zida Zhao2Haodong Huang3Wenfu Xu4School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics, Shenzhen 518055, China; Corresponding author at:School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, ChinaSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, ChinaSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, ChinaSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics, Shenzhen 518055, China; Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong, Shenzhen 518055, ChinaThis paper investigates the utilization of large language models (LLMs) for the comprehensive control of humanoid robot locomotion. Traditional reinforcement learning (RL) approaches for robot locomotion are resource-intensive and rely heavily on manually designed reward functions. To address these challenges, we propose a method that employs LLMs as the primary designer to handle key aspects of locomotion control, such as trajectory planning, inverse kinematics solving, and reward function design. By using user-provided prompts, LLMs generate and optimize code, reducing the need for manual intervention. Our approach was validated through simulations in Unity, demonstrating that LLMs can achieve human-level performance in humanoid robot control. The results indicate that LLMs can simplify and enhance the development of advanced locomotion control systems for humanoid robots.http://www.sciencedirect.com/science/article/pii/S2667379724000457Humanoid robotsLarge language modelsLocomotion controlReinforcement learning |
| spellingShingle | Shilong Sun Chiyao Li Zida Zhao Haodong Huang Wenfu Xu Leveraging large language models for comprehensive locomotion control in humanoid robots design Biomimetic Intelligence and Robotics Humanoid robots Large language models Locomotion control Reinforcement learning |
| title | Leveraging large language models for comprehensive locomotion control in humanoid robots design |
| title_full | Leveraging large language models for comprehensive locomotion control in humanoid robots design |
| title_fullStr | Leveraging large language models for comprehensive locomotion control in humanoid robots design |
| title_full_unstemmed | Leveraging large language models for comprehensive locomotion control in humanoid robots design |
| title_short | Leveraging large language models for comprehensive locomotion control in humanoid robots design |
| title_sort | leveraging large language models for comprehensive locomotion control in humanoid robots design |
| topic | Humanoid robots Large language models Locomotion control Reinforcement learning |
| url | http://www.sciencedirect.com/science/article/pii/S2667379724000457 |
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