• DocumentCode
    2281307
  • Title

    Online intention learning for human-robot interaction by scene observation

  • Author

    Awais, Muhammad ; Henrich, Dominik

  • Author_Institution
    Lehrstuhl fur Angewandte Inf. III, Univ. Bayreuth, Bayreuth, Germany
  • fYear
    2012
  • fDate
    21-23 May 2012
  • Firstpage
    13
  • Lastpage
    18
  • Abstract
    Intention recognition plays a key role in the cooperation among the humans. An intention describes an action or sequence of actions to be performed for achieving the intended purpose. The cooperating humans learn each others intentions while cooperation. In this paper we propose three ways how a robot can learn the intention of the cooperating human. In the first case, the robot learns the human intention by mapping the known human intention given in terms of scene information to the observed action sequence. The actions are already known to the robot. In the second case, the robot is only given the human actions but the robot estimates the human intention in terms of the changes that occur in the scene due to the human actions. The robot learns the human intention by mapping the observed action sequence to the human intention. The human intention is estimated from the scene information. In the third case, only the scene information is used in order to learn the human intention mapping. The scene information is used to infer the human actions as well as the human intention.
  • Keywords
    human-robot interaction; image sequences; learning (artificial intelligence); object recognition; robot vision; action sequence; human intention mapping; human-robot interaction; intention recognition; online intention learning; scene information; scene observation; Collaboration; Hidden Markov models; Humans; Indexes; Robots; Silicon; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Robotics and its Social Impacts (ARSO), 2012 IEEE Workshop on
  • Conference_Location
    Munich
  • ISSN
    2162-7568
  • Print_ISBN
    978-1-4673-0481-8
  • Type

    conf

  • DOI
    10.1109/ARSO.2012.6213391
  • Filename
    6213391