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.
CITATION STYLE
Dragan, A. D., Gordon, G. J., & Srinivasa, S. S. (2017). Learning from experience in manipulation planning: Setting the right goals. In Springer Tracts in Advanced Robotics (Vol. 100, pp. 309–326). Springer Verlag. https://doi.org/10.1007/978-3-319-29363-9_18
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