人間の生体力学を考慮した義肢制御のための予測モデル学習
Learning Predictive Models for Ergonomic Control of Prosthetic Devices
人間の筋骨格系への負担を予測しながら義肢の動作を最適化するモデル予測型インタラクションプリミティブを提案し、膝や筋力の負担を軽減できることを実機で示した。
著者: Geoffrey Clark, Joseph Campbell, Heni Ben Amor
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
We present Model-Predictive Interaction Primitives -- a robot learning framework for assistive motion in human-machine collaboration tasks which explicitly accounts for biomechanical impact on the human musculoskeletal system. First, we extend Interaction Primitives to enable predictive biomechanics: the prediction of future biomechanical states of a human partner conditioned on current observations and intended robot control signals. In turn, we leverage this capability within a model-predictive control strategy to identify the future ergonomic and biomechanical ramifications of potential robot actions. Optimal control trajectories are selected so as to minimize future physical impact on the human musculoskeletal system. We empirically demonstrate that our approach minimizes knee or muscle forces via generated control actions selected according to biomechanical cost functions. Experiments are performed in synthetic and real-world experiments involving powered prosthetic devices.
