Design and Control of a Muscle-skeleton Robot Elbow based on Reinforcement Learning

Abstract

The muscle-skeleton body structure and learning ability allow natural creatures to adapt to the complex environment. These can also make robots more adaptive in human-robot interaction scenarios. In this work, we implement a humanoid muscle-skeleton robot elbow joint actuated by two antagonistic pneumatic artificial muscles (PAMs). A reinforcement learning algorithm based on soft actor-critic (SAC) is adopted to learn the control policy of the proposed elbow joint. Lower action space and hindsight experience replay (HER) further reduce training time, and the temperature factor is fixed during the training process for small steady-state error. An elbow model is implemented in the simulation to verify the training procedure for our real robot elbow platform. The experimental results show that the RL learning procedure can learn control policies in the robot elbow prototype, and the steady-state error is within 0.64% after 1 s of control time.