Skill Generalization via Inference-based Planning

We present a novel approach which unifies conventional learning from demonstration (LfD) and motion planning using probabilistic inference, for generalizable skill reproduction. As a part of this approach, we also present a new probabilistic skill model that requires minimal parameter tuning, and is more suited for encoding skill constraints and performing inference in an efficient manner. Preliminary experimental results on a manipulation skill are also provided.

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