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

Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, Satoshi Nakamura

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)825-848
Number of pages24
JournalAdvanced Robotics
Volume25
Issue number6-7
DOIs
Publication statusPublished - 2011 Apr 20
Externally publishedYes

Keywords

  • hidden Markov model
  • Imitation learning
  • object manipulation
  • robot language acquisition

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications

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