Learning Two-Person Interaction Models for Responsive Synthetic Humanoids

Journal of virtual reality and broadcasting

Heni Ben Amor¹  Erik Berger²  David Vogt²  Bernhard Jung² 

[1] TU Darmstadt$$;TU Bergakademie Freiberg$$

关键词: humanoid robots;    imitation learning;    interaction learning;    motion adaptation;    motor learning;    virtual characters;   

DOI  :  10.20385/1860-2037/11.2014.1

学科分类：计算机科学（综合）

来源: Di P P - N R W

【 摘 要 】

Imitation learning is a promising approach for generating life-like behaviors of virtual humans and humanoid robots. So far, however, imitation learning has been mostly restricted to single agent settings where observed motions are adapted to new environment conditions but not to the dynamic behavior of interaction partners. In this paper, we introduce a new imitation learning approach that is based on the simultaneous motion capture of two human interaction partners. From the observed interactions, low-dimensional motion models are extracted and a mapping between these motion models is learned. This interaction model allows the real-time generation of agent behaviors that are responsive to the body movements of an interaction partner. The interaction model can be applied both to the animation of virtual characters as well as to the behavior generation for humanoid robots.

【 授权许可】

Unknown