Learning, generation and recognition of motions by reference-point- dependent probabilistic models

Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, Satoshi Nakamura

研究成果: Article査読

32 被引用数 (Scopus)


This paper presents a novel method for learning object manipulation such as rotating an object or placing one object on another. In this method, motions are learned using reference-point-dependent probabilistic models, which can be used for the generation and recognition of motions. The method estimates (i) the reference point, (ii) the intrinsic coordinate system type, which is the type of coordinate system intrinsic to a motion, and (iii) the probabilistic model parameters of the motion that is considered in the intrinsic coordinate system. Motion trajectories are modeled by a hidden Markov model (HMM), and an HMM-based method using static and dynamic features is used for trajectory generation. The method was evaluated in physical experiments in terms of motion generation and recognition. In the experiments, users demonstrated the manipulation of puppets and toys so that the motions could be learned. A recognition accuracy of 90% was obtained for a test set of motions performed by three subjects. Furthermore, the results showed that appropriate motions were generated even if the object placement was changed.

ジャーナルAdvanced Robotics
出版ステータスPublished - 2011

ASJC Scopus subject areas

  • ソフトウェア
  • 制御およびシステム工学
  • 人間とコンピュータの相互作用
  • ハードウェアとアーキテクチャ
  • コンピュータ サイエンスの応用


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