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arXiv AI By Anukriti Singh, Maxime Caniot, Pratap Tokekar, Firas Abi-Farraj

When, Where, and What to Correct: Human-Guided Adaptive Residual Correction for Robot Policies

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The paper introduces Adaptive Residual Correction (ARC), a lightweight post‑training module that adds a reinforcement‑learning–driven residual correction to a frozen imitation policy. ARC uses sparse human corrections to decide when the residual should activate, where in the residual action space to explore, and what the final residual action should be, thereby treating local failures as a localized learning problem rather than a global one. In experiments on three precision‑manipulation tasks, ARC raises average task success from 33.3 % to 75.0 % and confines the residual corrections to 2–3 dimensional subspaces that capture over 96 % of the correction magnitude. "whyItMatters":"The results demonstrate that sparse human guidance can effectively localize and constrain autonomous post‑training corrections, enabling significant performance gains without requiring full policy updates or dense human supervision."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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