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Active robot learning for building up high-order beliefs

  • Dayou Li
    ,
  • Beisheng Liu
    ,
  • Carsten Maple
    ,
  • Daming Jiang
    ,
  • Yong Yue
  • Beijing Jiaotong University
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

High-order beliefs of service robots regard the robots' thought about their users' intention and preference. The existing approaches to the development of such beliefs through machine learning rely on particular social cues or specifically defined award functions. Their applications can, therefore, be limited. This paper presents an active robot learning approach to facilitate the robots to develop the beliefs by actively collecting/discovering evidence they need. The emphasis is on active learning. Hence social cues and award functions are not necessary. Simulations show that the presented approach successfully enabled a robot to discover evidences it needs.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 05/11/2008

Publication status

Published - 05/11/2008

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9780769533056

ISBN (Electronic)

9780769533056

External Publication IDs

  • handle.net: 10547/270600
  • Scopus: 58149122934

Host publication title

nan

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