Learning to Walk With Deep Reinforcement Learning: Forward Dynamic Simulation of a Physics-Based Musculoskeletal Model of an Osseointegrated Transfemoral Amputee.

Abstract

This paper leverages the OpenSim physics-based simulation environment for the forward dynamic simulation of an osseointegrated transfemoral amputee musculoskeletal model, wearing a generic prosthesis. A deep reinforcement learning architecture, which combines the proximal policy optimization algorithm with imitation learning, is designed to enable the model to walk by using three different observation states. The first is a complete state that includes the agent's kinematics, ground reaction forces, and muscle data; the second is a reduced state that only includes the kinematics and ground reaction forces; the third is an augmented state that combines the kinematics and ground reaction forces with a prediction of the muscle data generated by a fully-connected feed-forward neural network. The empirical results demonstrate that the model trained with the augmented observation state can achieve walking patterns with rewards and gait symmetry ratings comparable to those of the model trained with the complete observation state, while there are no symmetric walking patterns when using the reduced observation state. This paper shows the importance of including muscle data in a deep reinforcement learning architecture for the forward dynamic simulation of musculoskeletal models of transfemoral amputees.