Warp RL: Reshaping Base Policy Distributions for Dynamics Adaptation
Residual reinforcement learning adapts a pretrained robot policy by learning an additive correction to its actions. While effective when adaptation amounts to shifting the base policy's action distribution, additive corrections cannot change the distribution's shape, scale, or state-dependent geometry -- limitations we formalize as wrong variance, miscalibrated confidence, and non-uniform correction. We show that these matter under dynamics shift: when the base distribution is geometrically mismatched to the shifted system, residual correction can underperform even the unadapted policy. We propose Warp RL, a policy adaptation method that replaces additive residuals with an invertible, sta...
| Authors | Ethan Hirschowitz, Fabio Ramos |
|---|---|
| Primary category | cs.LG |
| Published | 30 June 2026 |
| Last updated at source | 15 July 2026 |
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