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- W3214548617 abstract "Complex physical tasks entail a sequence of object interactions, each with its own preconditions -- which can be difficult for robotic agents to learn efficiently solely through their own experience. We introduce an approach to discover activity-context priors from in-the-wild egocentric video captured with human worn cameras. For a given object, an activity-context prior represents the set of other compatible objects that are required for activities to succeed (e.g., a knife and cutting board brought together with a tomato are conducive to cutting). We encode our video-based prior as an auxiliary reward function that encourages an agent to bring compatible objects together before attempting an interaction. In this way, our model translates everyday human experience into embodied agent skills. We demonstrate our idea using egocentric EPIC-Kitchens video of people performing unscripted kitchen activities to benefit virtual household robotic agents performing various complex tasks in AI2-iTHOR, significantly accelerating agent learning. Project page: this http URL" @default.
- W3214548617 created "2021-11-22" @default.
- W3214548617 creator A5012765543 @default.
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- W3214548617 date "2021-12-06" @default.
- W3214548617 modified "2023-09-29" @default.
- W3214548617 title "Shaping embodied agent behavior with activity-context priors from egocentric video" @default.
- W3214548617 hasPublicationYear "2021" @default.
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