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XIRL
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Cross-embodiment visual imitation via learned reward functions.
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author
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2026-02-18 17:00:48
raw text
XIRL: Cross-embodiment Inverse Reinforcement Learning XIRL: Cross-embodiment Inverse Reinforcement Learning Conference on Robot Learning (CoRL) 2021 Blog Paper Code Benchmark Poster Abstract . We investigate the visual cross-embodiment imitation setting, in which agents learn policies from videos of other agents (such as humans) demonstrating the same task, but with stark differences in their embodiments -- shape, actions, end-effector dynamics, etc. In this work, we demonstrate that it is possible to automatically discover and learn vision-based reward functions from cross-embodiment demonstration videos that are robust to these differences. Specifically, we present a self-supervised method for Cross-embodiment Inverse Reinforcement Learning (XIRL) that leverages temporal cycle-consistency constraints to learn deep visual embeddings that capture task progression from offline videos of demonstrations across multiple expert agents, each performing the same task di...
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