Modeling, Recognition and Supporting Trajectory Generation of Daily Object-handling based on Acquired Motion Models

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  • Sato Tomomasa
    The University of Tokyo, Graduate School of Information Science and Technology
  • Kubotera Hideyuki
    The University of Tokyo, Graduate School of Information Science and Technology
  • Harada Tatsuya
    The University of Tokyo, Graduate School of Information Science and Technology
  • Mori Taketoshi
    The University of Tokyo, Graduate School of Information Science and Technology

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  • 日常生活支援のための机上作業のモデル化およびその認識と支援軌道の生成
  • ニチジョウ セイカツ シエン ノ タメ ノ キジョウ サギョウ ノ モデルカ オヨビ ソノ ニンシキ ト シエン キドウ ノ セイセイ

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Abstract

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.

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