筋骨格制御の強化学習：機能シミュレーションによる肩の筋活性制御
Reinforcement Learning of Musculoskeletal Control from Functional Simulations
深層強化学習を用いて人体肩の筋骨格モデルの筋活性を制御する逆動力学コントローラを学習し、ランダムな角度軌道追従タスクで有効性を示した研究。
著者: Emanuel Joos, Fabien Péan, Orcun Goksel
分類: eess.SP, cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
To diagnose, plan, and treat musculoskeletal pathologies, understanding and reproducing muscle recruitment for complex movements is essential. With muscle activations for movements often being highly redundant, nonlinear, and time dependent, machine learning can provide a solution for their modeling and control for anatomy-specific musculoskeletal simulations. Sophisticated biomechanical simulations often require specialized computational environments, being numerically complex and slow, hindering their integration with typical deep learning frameworks. In this work, a deep reinforcement learning (DRL) based inverse dynamics controller is trained to control muscle activations of a biomechanical model of the human shoulder. In a generalizable end-to-end fashion, muscle activations are learned given current and desired position-velocity pairs. A customized reward functions for trajectory control is introduced, enabling straightforward extension to additional muscles and higher degrees of freedom. Using the biomechanical model, multiple episodes are simulated on a cluster simultaneously using the evolving neural models of the DRL being trained. Results are presented for a single-axis motion control of shoulder abduction for the task of following randomly generated angular trajectories.