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Learning from Experience in Manipulation Planning: Setting the Right Goals

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Robotics Research

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 100))

Abstract

In this paper, we describe a method of improving trajectory optimization based on predicting good initial guesses from previous experiences. In order to generalize to new situations, we propose a paradigm shift: predicting qualitative attributes of the trajectory that place the initial guess in the basin of attraction of a low-cost solution. We start with a key such attribute, the choice of a goal within a goal set that describes the task, and show the generalization capabilities of our method in extensive experiments on a personal robotics platform.

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Acknowledgments

This material is based upon work supported by DARPA-BAA-10-28, NSF-IIS-0916557, and NSF- EEC-0540865. Thanks to Chris Atkeson and the members of the Personal Robotics Lab for comments and fruitful discussions.

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Authors and Affiliations

  1. The Robotics Institute, Carnegie Mellon University, Pittsburgh, USA

    Anca D. Dragan & Siddhartha S. Srinivasa

  2. Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA

    Geoffrey J. Gordon

Authors
  1. Anca D. Dragan
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  2. Geoffrey J. Gordon
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  3. Siddhartha S. Srinivasa
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Corresponding author

Correspondence to Anca D. Dragan .

Editor information

Editors and Affiliations

  1. Robotics and Intelligent Machines, Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Henrik I. Christensen

  2. Department of Computer Science, Stanford University, Stanford, California, USA

    Oussama Khatib

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Dragan, A.D., Gordon, G.J., Srinivasa, S.S. (2017). Learning from Experience in Manipulation Planning: Setting the Right Goals. In: Christensen, H., Khatib, O. (eds) Robotics Research . Springer Tracts in Advanced Robotics, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-319-29363-9_18

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  • DOI: https://doi.org/10.1007/978-3-319-29363-9_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29362-2

  • Online ISBN: 978-3-319-29363-9

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