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arXiv · 2605.29605

VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models

Dehao Huang·Aoxiang Gu·Chengjie Zhang·Bolin Zou·Wenlong Dong·Zilang Cen·Yue Wang·Hong Zhang
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Abstract

Task-success confidence estimation for Vision-Language-Action (VLA) models provides a crucial task-level signal for monitoring manipulation in open-world environments and supporting downstream decision-making. Existing methods typically construct task-success confidence from action-token probabilities. However, such probabilities are not naturally available in flow-matching policies, limiting their applicability to mainstream flow-matching VLAs. To address this issue, we propose VLAConf, a two-stage representation-level confidence framework that operates on frozen pretrained VLA representations. A step-conditioned Coin-Flip Network learns an uncalibrated inverse success-support score from successful demonstrations, while a low-capacity calibrator fitted on outcome-labeled successful and failed rollouts maps the aggregated score to task-success probability. Experimental results on the LIBERO benchmark demonstrate that VLAConf improves online task-success confidence estimation over alternative approaches. We further demonstrate its utility in selective expert assistance, where confidence-triggered handoffs improve task success over no intervention. Its applicability is also evaluated in real-robot experiments. To access the source code and supplementary videos, visit https://sites.google.com/view/vlaconf.

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Dehao Huang, Aoxiang Gu, Chengjie Zhang, Bolin Zou, Wenlong Dong, Zilang Cen, Yue Wang, Hong Zhang. 2026-05-28. VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models. https://arxiv.org/abs/2605.29605

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