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Learning from Demonstration (LfD) enables robots to acquire versatile skillsby learning motion policies from human demonstrations. It endows users with anintuitive interface to transfer new skills to robots without the need fortime-consuming robot programming and inefficient solution exploration. Duringtask executions, the robot motion is usually influenced by constraints imposedby environments. In light of this, task-parameterized LfD (TP-LfD) encodesrelevant contextual information into reference frames, enabling better skillgeneralization to new situations. However, most TP-LfD algorithms typicallyrequire multiple demonstrations across various environmental conditions toensure sufficient statistics for a meaningful model. It is not a trivial taskfor robot users to create different situations and perform demonstrations underall of them. Therefore, this paper presents a novel algorithm to learn skillsfrom few demonstrations. By leveraging the reference frame weights that capturethe frame importance or relevance during task executions, our methoddemonstrates excellent skill acquisition performance, which is validated inreal robotic environments.

Learning from Few Demonstrations with Frame-Weighted Motion Generation