Simulated bipedal exoskeleton gait learning using Reinforcement Learning techniques

Abstract

This paper introduces a Reinforcement Learning (RL) approach as a method for training a computer simulation of a bipedal lower limb exoskeleton to walk, without explicit supervision. A simulation of a bipedal exoskeleton with six joints (left and right hip, knee, and ankle) was developed, including an implementation of the Proximal Policy Optimization algorithm for training the RL agent. The system was implemented using Python and OpenAI Gym library, which provided an environment to simulate interactions and learn locomotion dynamics. The RL-trained agent is capable of learning stable locomotion by interacting with the simulated environment and a complex reward system. The results demonstrate the potential of RL for adaptive control of exoskeletons and serve as a foundation for further research in exoskeleton control and training.