Autonomous online generation of a motor representation of the workspace for intelligent whole-body reaching

Lorenzo Jamone, Martim Brandao, Lorenzo Natale, Kenji Hashimoto, Giulio Sandini, Atsuo Takanishi

研究成果: Article査読

17 被引用数 (Scopus)

抄録

We describe a learning strategy that allows a humanoid robot to autonomously build a representation of its workspace: we call this representation Reachable Space Map. Interestingly, the robot can use this map to: (i) estimate the Reachability of a visually detected object (i.e. judge whether the object can be reached for, and how well, according to some performance metric) and (ii) modify its body posture or its position with respect to the object to achieve better reaching. The robot learns this map incrementally during the execution of goal-directed reaching movements; reaching control employs kinematic models that are updated online as well. Our solution is innovative with respect to previous works in three aspects: the robot workspace is described using a gaze-centered motor representation, the map is built incrementally during the execution of goal-directed actions, learning is autonomous and online. We implement our strategy on the 48-DOFs humanoid robot Kobian and we show how the Reachable Space Map can support intelligent reaching behavior with the whole-body (i.e. head, eyes, arm, waist, legs).

本文言語English
ページ(範囲)556-567
ページ数12
ジャーナルRobotics and Autonomous Systems
62
4
DOI
出版ステータスPublished - 2014 4

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mathematics(all)
  • Computer Science Applications

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