表情豊かな人間-ロボット相互作用のための手振りジェスチャの学習・生成・適応
Learning,Generating and Adapting Wave Gestures for Expressive Human-Robot Interaction
人間の関節角度データからリズミカルな手振り動作を確率的に学習・生成し、周波数領域で腕の姿勢や振り頻度・振幅を変調できる模倣学習手法を提案した。6自由度マニピュレータのシミュレーション実験で有効性を確認した。
著者: Mihalis Panteris, Simon Manschitz, Sylvain Calinon
分類: cs.RO
原文アブストラクト
This study proposes a novel imitation learning approach for the stochastic generation of human-like rhythmic wave gestures and their modulation for effective non-verbal communication through a probabilistic formulation using joint angle data from human demonstrations. This is achieved by learning and modulating the overall expression characteristics of the gesture (e.g., arm posture, waving frequency and amplitude) in the frequency domain. The method was evaluated on simulated robot experiments involving a robot with a manipulator of 6 degrees of freedom. The results show that the method provides efficient encoding and modulation of rhythmic movements and ensures variability in their execution.
