周期的動作の予測モデリングによる人とロボットの共生歩行
Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking
歩行のような周期的動作を確率的に学習するPeriodic Interaction Primitivesを提案し、人間の歩行予測やロボット義足の制御に応用して高精度・高速な推論を実現した。
著者: Geoffrey Clark, Joseph Campbell, Seyed Mostafa Rezayat Sorkhabadi, Wenlong Zhang, Heni Ben Amor
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior. Our approach extends existing formulations of Interaction Primitives to periodic movement regimes, i.e., walking. We show that this model is particularly well-suited for learning data-driven, customized models of human walking, which can then be used for generating predictions over future states or for inferring latent, biomechanical variables. We also demonstrate how the same framework can be used to learn controllers for a robotic prosthesis using an imitation learning approach. Results in experiments with human participants indicate that Periodic Interaction Primitives efficiently generate predictions and ankle angle control signals for a robotic prosthetic ankle, with MAE of 2.21 degrees in 0.0008s per inference. Performance degrades gracefully in the presence of noise or sensor fall outs. Compared to alternatives, this algorithm functions 20 times faster and performed 4.5 times more accurately on test subjects.
