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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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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