Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning
This work addresses the problem of creating accurate virtual human models for applications like robotics or biomechanics, though it appears incremental as it builds on existing reinforcement learning methods.
The paper tackled generating realistic 3D human walking motion in simulation by using deep reinforcement learning with policy learning and parameter identification to match real-world biomechanical data, achieving results that generalize across subjects with different kinematic structures and gait characteristics.
In this work, we develop an automated method to generate 3D human walking motion in simulation which is comparable to real-world human motion. At the core, our work leverages the ability of deep reinforcement learning methods to learn high-dimensional motor skills while being robust to variations in the environment dynamics. Our approach iterates between policy learning and parameter identification to match the real-world bio-mechanical human data. We present a thorough evaluation of the kinematics, kinetics and ground reaction forces generated by our learned virtual human agent. We also show that the method generalizes well across human-subjects with different kinematic structure and gait-characteristics.