logo Idiap Research Institute        
 [BibTeX] [Marc21]
Learning, Generating and Adapting Wave Gestures for Expressive Human-Robot Interaction
Type of publication: Conference paper
Citation: Panteris_HRI_2020
Publication status: Published
Booktitle: Proc. ACM/IEEE Intl Conf. on Human-Robot Interaction (HRI)
Year: 2020
Pages: 386-388
URL: https://dl.acm.org/doi/10.1145...
DOI: 10.1145/3371382.3378286
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.
Keywords: human-robot interaction, movement primitives
Projects Idiap
Authors Panteris, M.
Manschitz, S.
Calinon, Sylvain
Added by: [UNK]
Total mark: 0
  • Panteris_HRI_2020.pdf