Learning task space control through goal directed exploration

Lorenzo Jamone*, Lorenzo Natale, Kenji Hashimoto, Giulio Sandini, Atsuo Takanishi

*この研究の対応する著者

研究成果: Conference contribution

18 被引用数 (Scopus)

抄録

We present an autonomous goal-directed strategy to learn how to control a redundant robot in the task space. We discuss the advantages of exploring the state space through goal-directed actions defined in the task space (i.e. learning by trying to do) instead of performing motor babbling in the joints space, and we stress the importance of learning to be performed online, without any separation between training and execution. Our solution relies on learning the forward model and then inverting it for the control; different approaches to learn the forward model are described and compared. Experimental results on a simulated humanoid robot are provided to support our claims. The robot learns autonomously how to perform reaching actions directed toward 3D targets in task space by using arm and waist motion, not relying on any prior knowledge or initial motor babbling. To test the ability of the system to adapt to sudden changes both in the robot structure and in the perceived environment we artificially introduce two different kinds of kinematic perturbations: a modification of the length of one link and a rotation of the task space reference frame. Results demonstrate that the online update of the model allows the robot to cope with such situations.

本文言語English
ホスト出版物のタイトル2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011
ページ702-708
ページ数7
DOI
出版ステータスPublished - 2011 12 1
イベント2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011 - Phuket, Thailand
継続期間: 2011 12 72011 12 11

出版物シリーズ

名前2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011

Conference

Conference2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011
国/地域Thailand
CityPhuket
Period11/12/711/12/11

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識

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