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Learning,Generating and Adapting Wave Gestures for Expressive Human-Robot Interaction

2020-03-04 22:09:00
Mihalis Panteris, Simon Manschitz, Sylvain Calinon
arXiv_RO
       
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2003.02348

PDF

https://arxiv.org/pdf/2003.02348.pdf

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