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title
Human-to-Robot Imitation in the Wild
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Human-to-Robot Imitation in the Wild
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Human-to-Robot Imitation in the Wild
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2026-03-11 23:45:58
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Human-to-Robot Imitation in the Wild Human-to-Robot Imitation in the Wild Shikhar Bahl Abhinav Gupta* Deepak Pathak* Carnegie Mellon University RSS 2022 Paper arXiv Demo Video RSS Talk Summary Dataset Code (Coming Soon) Can robots learn manipulation from watching humans? 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 Imitating Robot Learning. WHIRL extracts a prior over the intent of the human demonstrator, using it to initialize our agent's...
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