Warp: Structured Output-Space Adaptation for Frozen Robot Policies

The authors propose Warp, a context-conditioned monotone bijective spline adapter that nonlinearly reshapes the sampled outputs of a frozen base policy while preserving per-dimension ordering. Across four ManiSkill tasks, Warp achieves the strongest recovery in a matched evolution-strategy adapter study; after switching to PPO, the ordering relationships vary by task. In a physical SO101 peg insertion task, Warp's success rate is comparable to a strong stochastic residual, with median time per successful cycle reduced by about 30%.