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Learning of Motion Primitives Using Reference-Point-Dependent GP-HSMM for Domestic Service Robots

  • Kensuke Iwata
  • Tomoaki Nakamura
  • Takayuki Nagai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

In this paper, we propose a method for motion learning aimed at the execution of autonomous household chores by home robots in real environments. For robots to act autonomously in a real environment, it is necessary to define appropriate actions for the environment. However, it is difficult to define these actions manually. Therefore, body motions that are common to multiple actions are defined as motion primitives. Complex actions can then be learned by combining these motion primitives. For learning motion primitives, we propose reference-point-dependent Gaussian process hidden semi-Markov model (RPD-GP-HSMM). For verification, a robot is tele-operated in order to perform actions included in several domestic household chores. The robot then learned the associated motion primitives from the robot’s body information and object information.

Keywords

Motion learning Gaussian process Hidden semi-Markov model Reference-point 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kensuke Iwata
    • 1
  • Tomoaki Nakamura
    • 1
  • Takayuki Nagai
    • 1
  1. 1.The University of Electro-CommunicationChofuJapan

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