cs.ROOct 2, 2026

Does a Learned Corrector Beat a Simple Retreat? Evidence from a Frozen VLA

Authors: Chenchao Sheng, Zhuang Jiang, Liuhaichen Yang, Ningwei Bai, Zezhi Tang

Organizations: University College London (UCL), London, United Kingdom

Abstract

Before deploying runtime recovery for a frozen vision-language-action (VLA) policy, one must establish that an intervention improves success beyond ordinary run-to-run variation and that its complexity adds value over a simple action. We evaluate these questions on frozen π0.5π_{0.5} across four RoboTwin tasks. For each test seed, we pair rollouts with and without correction and include a same-seed base-policy re-run as a placebo. Seed-cluster intervals and prespecified comparison rules assess net gains against stochastic outcome changes. Across 3,888 paired episodes, the full pipeline raises success on beat_allowbreak block_allowbreak hammer by +13.5+13.5,pp (95% interval [+9.4,+17.7][+9.4,+17.7]), with no detectable gain on the other three tasks at the deployed weight. Among failed base episodes on the responsive task, 43.2%43.2\% succeed on a plain re-run, compared with 63.5%63.5\% after correction; many nominal rescues therefore reflect the base policy's own variability. A fixed-time trigger and scripted return to an earlier joint configuration produce a net gain with no detected difference from the learned pipeline across two rounds, although our prespecified equivalence criterion is not met consistently. Pausing and a constant-action control do not yield comparable gains. On this benchmark, the decision to intervene depends strongly on the task, and a paired placebo plus a simple retreat baseline are needed to establish what learned correction contributes.

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