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History of Robotics Research and Development of Japan2006Integration, Intelligence, etc.Modeling, Recognition and Supporting Trajectory Generation of Daily Object-handling based on Acquired Motion Models

Tomomasa SatoThe University of Tokyo
Hideyuki KuboteraTOYOTA MOTOR CORPORATION
Tatsuya HaradaThe University of Tokyo
Taketoshi MoriThe University of Tokyo
This paper proposes a robotic assistance system for object handling based on imitative learning. At first, the system learns temporally short segments of motion called“motion primitives”from observation of human object handling tasks. Secondly daily human object-handling is recognized as a sequence of motion primitives. Then the occurrence of an appropriate assisting task defined as a sequence of motion primitives is predicted. Finally the corresponding assisting trajectory is generated from the sequence of motion primitives. The system is composed of such algorithms as object handling motion clustering, human motion recognition, assisting task prediction and trajectory generation, which are learned from human motion. On the other hand, the user specifies the tasks beforehand which the system should support. The validity of the proposed algorithms is confirmed through the experiment of object-handling assistance utilizing a cup. 23th RSJ Best Paper Award  
Recognition, prediction and execution
Recognition, prediction and execution

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Recognition, prediction and execution

Correspondence papers

Tomomasa Sato, Hideyuki Kubotera, Tatsuya Harada, and Taketoshi Mori：Modeling, Recognition and Supporting Trajectory Generation of Daily Object-handling based on Acquired Motion Models

Journal of the Robotics Society of Japan, Vol. 25, No. 1, 2007 （in Japanese）.

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