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WHIRL: Human-to-Robot Imitation in the Wild. Published at RSS 2022.

Project Website: Authors: Shikhar Bahl, Abhinav Gupta, Deepak Pathak Affiliation: Carnegie Mellon University Project Abstract: We approach the problem of learning by watching humans in the wild. While traditional approaches in Imitation and Reinforcement Learning are promising for learning in the real world, they are either sample inefficient or are constrained to lab settings. Meanwhile, there has been a lot of success in processing passive, unstructured human data. We propose tackling this problem via an efficient one-shot robot learning algorithm, centered around learning from a third-person perspective. We call our method WHIRL: In the Wild Human-Imitated Robot Learning. In WHIRL, we aim to use human videos to extract a prior over the intent of the demonstrator and use this to initialize our agent's policy. We introduce an efficient real-world policy learning scheme, that improves over the human prior using interactions. Our key contributions are a simple sampling-based policy optimization approach, a novel objective function for aligning human and robot videos as well as an exploration method to boost sample efficiency. We show one-shot generalization, and success in real-world settings, including 20 different manipulation tasks in the wild. Music by Vincent Rubinetti Download the music on Bandcamp: Stream the music on Spotify:

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