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

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arXiv:2610.06921v1 Announce Type: new 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 $\pi_{0.5}$ 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 $+13.5$\,pp (95\% interval $[+9.4,+17.7]$), with no detectable gain on the other three tasks at t…

SourcearXiv RoboticsAuthor: Chenchao Sheng, Zhuang Jiang, Liuhaichen Yang, Ningwei Bai, Zezhi Tang
Does a Learned Corrector Beat a Simple Retreat? Evidence from a Frozen VLA
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[Submitted on 2 Oct 2026]

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

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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 $\pi_{0.5}$ 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 $+13.5$\,pp (95\% interval $[+9.4,+17.7]$), with no detectable gain on the other three tasks at the deployed weight. Among failed base episodes on the responsive task, $43.2\%$ succeed on a plain re-run, compared with $63.5\%$ 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.

Comments: 15 pages, 6 figures

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2610.06921 [cs.RO]

(or arXiv:2610.06921v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.06921

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Ningwei Bai [view email] [v1] Fri, 2 Oct 2026 21:57:32 UTC (232 KB)

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  • AI generation is temporarily unavailable; this entry was preserved with deterministic fallback metadata.
  • arXiv:2610.06921v1 Announce Type: new Abstract: Before deploying runtime recovery for a frozen vision-language-action (VLA) policy, one must establish that an intervention improve…

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