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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Bibliographic Details
Published in:Biomimetic intelligence and robotics 2024-12, Vol.4 (4), p.100187, Article 100187
Main Authors: Sun, Shilong, Li, Chiyao, Zhao, Zida, Huang, Haodong, Xu, Wenfu
Format: Article
Language:English
Subjects:
Humanoid robots
Large language models
Locomotion control
Reinforcement learning
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Summary: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.
ISSN:2667-3797
2667-3797
DOI:10.1016/j.birob.2024.100187