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The International Journal of Robotics Research
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Recognizing Assembly Tasks Through Human Demonstration

Jun Takamatsu

Institute of Industrial Science the University of Tokyo Tokyo, Japan, j-taka,ogawara}@cvl.iis.u-tokyo.ac.jp

Koichi Ogawara

Institute of Industrial Science the University of Tokyo Tokyo, Japan, j-taka,ogawara}@cvl.iis.u-tokyo.ac.jp

Hiroshi Kimura

Graduate School of Information Systems the University of Electro-Communications Tokyo, Japan, hiroshi{at}kimura.is.uec.ac.jp

Katsushi Ikeuchi

Graduate School of Interdisciplinary Information Studies the University of Tokyo Tokyo, Japan, ki{at}cvl.iis.u-tokyo.ac.jp

As one of the methods for reducing the work of programming, the Learning-from-Observation (LFO) paradigm has been heavily promoted. This paradigm requires the programmer only to perform a task in front of a robot and does not require expertise. In this paper, the LFO paradigm is applied to assembly tasks by two rigid polyhedral objects. A method is proposed for recognizing these tasks as a sequence of movement primitives from noise-contaminated data obtained by a conventional 6 degree-of-freedom (DOF) object-tracking system. The system is implemented on a robot with a real-time stereo vision system and dual arms with dexterous hands, and its effectiveness is demonstrated.

Key Words: assembly planning • learning from observation • movement primitives

The International Journal of Robotics Research, Vol. 26, No. 7, 641-659 (2007)
DOI: 10.1177/0278364907080736
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