Causal-ACT masks task-irrelevant image features and lifts out-of-distribution transfer success from 0.23 to 0.82 in a simulated ALOHA cube transfer task.
On feature learning in the presence of spurious correlations.Advances in Neural Information Processing Systems, 35: 38516–38532, 2022
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Improving Generalization Ability of Robotic Imitation Learning by Resolving Causal Confusion in Observations
Causal-ACT masks task-irrelevant image features and lifts out-of-distribution transfer success from 0.23 to 0.82 in a simulated ALOHA cube transfer task.