High-Precision Control of Humanoid Muscle-skeleton Robotic Arm Using Reinforcement Learning and Large Language Models

Abstract

In recent years, humanoid robotics have achieved significant advancements, including those embodied by muscle-skeleton robots. Due to the nonlinear characteristics of these robots, reinforcement learning is a popular method for muscle-skeleton robot control. However, the design of the reward function and the adjustment of hyper parameters is a difficult task when using reinforcement learning. In this paper, we combine reinforcement learning with a large language model to achieve precise control of a humanoid muscle-skeleton robotic arm. We first tell the large language model task, states, and hyper parameters, and then continuously feedback the training results. We can finally get a controller that can control the robot to complete the target-tracking task. The experimental results show that the large language model can propose a suitable reward function based on experience, and adjust the hyper parameters to better training results within several questions and answers.