CalVLA: Shaping Native Confidence in Vision-Language-Action Policies with Outcome Supervision

Abstract:Can a vision-language-action (VLA) policy signal likely task failure from its own action probabilities? We introduce CalVLA, which shapes native action-token confidence using successful and failed rollouts and an Action-Confidence Alignment (ACal) squared margin-ranking loss. Low-rank adapters update OpenVLA-7B without an auxiliary failure detector. Observations are paired within tasks at similar normalized elapsed times, not necessarily equivalent physical progress. On a held-out test split from 500 LIBERO-10 configurations, adapted confidence achieves a joint failure-discrimination AUROC of $0.7392\pm0.0480$ over three seeds, averaging scores at 130 and 260 steps. Frozen-policy probes reach 0.6146--0.6436, but superiority over the linear probe is statistically inconclusive. For the same confidence summary, AUROC rises from 0.4485 before adaptation to 0.7327 after adaptation. Mean task success decreases from 58% to 55%. These results show improved failure discrimination, not improved control or established safety benefits.

8 pages, 2 figures. Accepted at the Science of Physical AI Safety (SPAIS) Workshop, CoRL 2026

Artificial Intelligence (cs.AI); Robotics (cs.RO)

arXiv:2611.00394 [cs.AI]

(or arXiv:2611.00394v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.00394 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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