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原文摘要

Robot policies that remain broadly competent after deployment can degrade when dynamics, contact, geometry, actuation, or sensing differ from source conditions. We study post-hoc adaptation through structured output-space adapters: compact transformations of samples from a frozen base policy. We introduce Warp, a context-conditioned monotone bijective spline that enables nonlinear sample-dependent reshaping while preserving per-dimension ordering; Translation and Affine provide location-only and location--scale references. Across four ManiSkill tasks with controlled deployment shifts, Warp achieves the strongest recovery in a matched evolution-strategy (ES) adapter study, outperforming controls for spline parameterization, location--scale adaptation, and generic nonlinear capacity. Under PPO, the ordering becomes task dependent, showing that richer adapter structure does not automatically translate into better adaptation under a different optimizer. Finally, we test the practical benefit of this approach on physical SO101 peg insertion. Within an interaction-limited RLPD/SAC pipeline, Warp achieves comparable success to a strong stochastic residual while reducing median successful cycle time by approximately 30%. These results identify structured output-space adaptation as a useful design space for adapting frozen robot policies.