Learning task manifolds for constrained object manipulation

Miao Li, Kenji Tahara, Aude Billard

研究成果: Contribution to journalArticle査読

1 被引用数 (Scopus)

抄録

Reliable physical interaction is essential for many important challenges in robotic manipulation. In this paper, we consider Constrained Object Manipulations tasks (COM), i.e. tasks for which constraints are imposed on the grasped object rather than on the robot’s configuration. To enable robust physical interaction with the environment, this paper presents a manifold learning approach to encode the COM task as a vector field. This representation enables an intuitive task-consistent adaptation based on an object-level impedance controller. Simulations and experimental evaluations demonstrate the effectiveness of our approach for several typical COM tasks, including dexterous manipulation and contour following.

本文言語英語
ページ(範囲)159-174
ページ数16
ジャーナルAutonomous Robots
42
1
DOI
出版ステータス出版済み - 1 1 2018

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

フィンガープリント 「Learning task manifolds for constrained object manipulation」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

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