| 要約: | 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.
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