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arXiv · 1911.11744

Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration

Simon Stepputtis·Joseph Campbell·Mariano Phielipp·Chitta Baral·Heni Ben Amor
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Abstract

In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a task, which in turn is used to synthesize specific motion controllers at run-time. This multimodal approach enables generalization to a wide variety of environmental conditions and allows an end-user to direct a robot policy through verbal communication. We empirically validate our approach with an extensive set of simulations and show that it achieves a high task success rate over a variety of conditions while remaining amenable to probabilistic interpretability.

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BibTeXRIS

Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Chitta Baral, Heni Ben Amor. 2019-11-26. Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration. https://arxiv.org/abs/1911.11744

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