Confidence Calibration in Vision-Language-Action Models

Abstract:Trustworthy robot behavior requires not only high levels of task success but also that the robot can reliably quantify how likely it is to succeed. To this end, we present a first-of-its-kind study of confidence calibration in vision-language-action (VLA) foundation models, which map visual observations and natural language instructions to low-level robot motor commands. We establish a confidence estimation baseline for VLAs, examine how task success relates to calibration error and how calibration evolves over time, and introduce two lightweight techniques to remedy the miscalibration we observe: prompt ensembles and action-wise Platt scaling. Our aim in this study is to begin to develop the tools and conceptual understanding necessary to render VLAs trustworthy via reliable uncertainty quantification.

Robotics (cs.RO); Machine Learning (cs.LG)

arXiv:2507.17383 [cs.RO]

(or arXiv:2507.17383v3 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2507.17383 Focus to learn more arXiv-issued DOI via DataCite

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