When, Where, and What to Correct: Human-Guided Adaptive Residual Correction for Robot Policies
Adaptive Residual Correction is introduced, a lightweight post-training module that uses reinforcement learning (RL) to learn a residual correction on top of a frozen policy while using sparse human corrections to structure autonomous exploration.
Anukriti Singh, Maxime Caniot, Pratap Tokekar et al.
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