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Confidence Calibration in Vision-Language-Action Models
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arXiv:2507.17383v3 Announce Type: replace 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.
Source：https://arxiv.org/abs/2507.17383PreviousMulti-Agent Reinforcement Learning-Based UAV Pathfinding for Obstacle Avoidance in Stochastic EnvironmentNextTRAIN: Terrain-Aware Interactive Navigation for Quadruped Robots via Reinforcement Learning
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