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Autonomous Robots

, Volume 42, Issue 1, pp 159–174 | Cite as

Learning task manifolds for constrained object manipulation

  • Miao LiEmail author
  • Kenji Tahara
  • Aude Billard
Article

Abstract

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.

Keywords

Task manifold Impedance learning Constrained object manipulation Task-consistent adaptation 

Notes

Acknowledgements

This work was supported by the European Union Seventh Framework Programme FP7/2007-2013 under Grant Agreement No. 288533 ROBOHOW.COG.

Supplementary material

10514_2017_9643_MOESM1_ESM.wmv (9.6 mb)
Supplementary material 1 (wmv 9826 KB)

Supplementary material 2 (wmv 2967 KB)

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Learning Algorithms and Systems Laboratory(LASA)École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
  2. 2.Faculty of EngineeringKyushu UniversityFukuokaJapan

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