TEAM: ユーザ実演から協働ロボットの動作を教示するパラメータフリーアルゴリズム
TEAM: a parameter-free algorithm to teach collaborative robots motions from user demonstrations
確率的動作プリミティブに基づき、Jensen-Shannonダイバージェンスとベイズ最適化でパラメータを自動決定する、手動調整不要の教示学習法を提案し、エレベータ保守や工場作業の実証実験で精度と使いやすさを示した。
著者: Lorenzo Panchetti, Jianhao Zheng, Mohamed Bouri, Malcolm Mielle
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Learning from demonstrations (LfD) enables humans to easily teach collaborative robots (cobots) new motions that can be generalized to new task configurations without retraining. However, state-of-the-art LfD methods require manually tuning intrinsic parameters and have rarely been used in industrial contexts without experts. We propose a parameter-free LfD method based on probabilistic movement primitives, where parameters are determined using Jensen-Shannon divergence and Bayesian optimization, and users do not have to perform manual parameter tuning. The cobot's precision in reproducing learned motions, and its ease of teaching and use by non-expert users are evaluated in two field tests. In the first field test, the cobot works on elevator door maintenance. In the second test, three factory workers teach the cobot tasks useful for their daily workflow. Errors between the cobot and target joint angles are insignificant -- at worst 0.28 deg -- and the motion is accurately reproduced -- GMCC score of 1. Questionnaires completed by the workers highlighted the method's ease of use and the accuracy of the reproduced motion. Public implementation of our method and datasets are made available online.
