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Supervising Robot Learning with Language and Video from the Web

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If you have a question about this talk, please contact Marinela Parovic.

While deep reinforcement learning applied to robotics has seen a number of recent successes in constrained environments, generalist robots that can operate in diverse real world settings have remained out of reach. Critically, robot learning algorithms have yet to be able to learn from a sufficient breadth of data that can enable broad generalization across tasks and environments. In this talk, I’ll discuss the paradigm of offline learning for robotics as a path towards generalist robots, and how we might supervise this offline learning process in a scalable way using crowdsourced language and videos of humans. Specifically, I’ll cover two recent papers which learn reward functions for offline reinforcement learning through language annotations of pre-collected robot datasets and human video datasets which exist on the web.

This talk is part of the Language Technology Lab Seminars series.

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