• DocumentCode
    117237
  • Title

    Fall avoidance of bipedalwalking robot by profit sharing that can learn deterministic policy for POMDPs environments

  • Author

    Suzuki, Takumi ; Osana, Yuko

  • Author_Institution
    Sch. of Comput. Sci., Tokyo Univ. of Technol., Tokyo, Japan
  • fYear
    2014
  • fDate
    July 30 2014-Aug. 1 2014
  • Firstpage
    184
  • Lastpage
    189
  • Abstract
    In this paper, fall avoidance of bipedal walking robot is realized by the Profit Sharing that can learn deterministic policy for POMDPs environments. In this research, the Profit Sharing that can learn deterministic policy for POMDPs environments which can obtain the deterministic policy by using the history of observations is employed. We carried out a series of experiments using bipedal walking robot, and confirmed that attitude control can be realized by the Profit Sharing that can learn deterministic policy for POMDPs environments.
  • Keywords
    attitude control; learning (artificial intelligence); legged locomotion; POMDP environments; attitude control; bipedal walking robot; deterministic policy; fall avoidance; observation history; profit sharing; Accelerometers; Robots; Bipedal Walking Robot; Fall Avoidance; Reinforcement Learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Nature and Biologically Inspired Computing (NaBIC), 2014 Sixth World Congress on
  • Conference_Location
    Porto
  • Print_ISBN
    978-1-4799-5936-5
  • Type

    conf

  • DOI
    10.1109/NaBIC.2014.6921875
  • Filename
    6921875
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=117237