Warp: Structured Output-Space Adaptation for Frozen Robot Policies
Overview
This paper introduces Warp, a context-conditioned monotone bijective spline designed to adapt frozen robot policies through structured output-space transformations. The authors evaluate Warp across four ManiSkill tasks with controlled deployment shifts and on physical SO101 peg insertion experiments. Results show that Warp achieves the strongest recovery compared to matched evolution-strategy adapters and generic nonlinear controls in simulation, while reducing median successful cycle time by approximately 30% in physical trials under an interaction-limited RLPD/SAC pipeline. The study also notes that adaptation performance becomes task-dependent when using PPO, indicating that richer adapter structures do not universally improve adaptation across different optimizers.
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Reported quantity · percent: 30 other · Basis not reported
“cle time by approximately 30%. These results identify structured output-space adaptation as a useful design space for adapting frozen robot policies.”
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Reported quantity · tasks: 4 other · reported trials
“hat 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”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalWarp: Structured Output-Space Adaptation for Frozen Robot Policies
This paper introduces Warp, a context-conditioned monotone bijective spline designed to adapt frozen robot policies through structured output-space transformations. The authors evaluate Warp across four ManiSkill tasks with controlled deployment shifts and on physical SO101 peg insertion experiments. Results show that Warp achieves the strongest recovery compared to matched evolution-strategy adapters and generic nonlinear controls in simulation, while reducing median successful cycle time by approximately 30% in physical trials under an interaction-limited RLPD/SAC pipeline. The study also notes that adaptation performance becomes task-dependent when using PPO, indicating that richer adapter structures do not universally improve adaptation across different optimizers.
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