強化学習による歩行時の人間の運動力学・運動学のモデリング
Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning
深層強化学習とパラメータ同定を組み合わせ、実世界の生体力学的データに一致する3D人間歩行運動をシミュレーション上で生成する手法を開発した。
著者: Visak Kumar
分類: cs.RO, cs.LG
原文アブストラクト
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.
