• DocumentCode
    636950
  • Title

    Bio-robots automatic navigation with graded electric reward stimulation based on Reinforcement Learning

  • Author

    Chen Zhang ; Chao Sun ; Liqiang Gao ; Nenggan Zheng ; Weidong Chen ; Xiaoxiang Zheng

  • Author_Institution
    Qiushi Acad. for Adv. Studies (QAAS), Zhejiang Univ., Hangzhou, China
  • fYear
    2013
  • fDate
    3-7 July 2013
  • Firstpage
    6901
  • Lastpage
    6904
  • Abstract
    Bio-robots based on brain computer interface (BCI) suffer from the lack of considering the characteristic of the animals in navigation. This paper proposed a new method for bio-robots´ automatic navigation combining the reward generating algorithm base on Reinforcement Learning (RL) with the learning intelligence of animals together. Given the graded electrical reward, the animal e.g. the rat, intends to seek the maximum reward while exploring an unknown environment. Since the rat has excellent spatial recognition, the rat-robot and the RL algorithm can convergent to an optimal route by co-learning. This work has significant inspiration for the practical development of bio-robots´ navigation with hybrid intelligence.
  • Keywords
    biocontrol; bioelectric phenomena; biological techniques; brain-computer interfaces; learning (artificial intelligence); learning systems; robot dynamics; RL algorithm; Reinforcement Learning; bio-robot automatic navigation; brain computer interface; co-learning; graded electric reward stimulation; hybrid intelligence; learning intelligence; rat-robot; reward generating algorithm; spatial recognition; Conferences; Learning (artificial intelligence); Mobile robots; Navigation; Rats;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
  • Conference_Location
    Osaka
  • ISSN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/EMBC.2013.6611144
  • Filename
    6611144