Temporal Difference Calibration in Sequential Tasks: Application to Vision-Language-Action Models
Recent advances in vision-language-action (VLA) models for robotics have highlighted the importance of reliable uncertainty quantification in sequential tasks. However, assessing and improving calibration in such settings remains mostly unexplored, especially when only partial trajectories are observed. In this work, we formulate sequential calibration for episodic tasks, where task-success confidence is produced along an episode, while success is determined at the end of it. We introduce a sequential extension of the Brier score and show that, for binary outcomes, its risk minimizer coincides with the VLA policy's value function. This connection bridges uncertainty calibration and reinfo...
| Authors | Shelly Francis-Meretzki, Mirco Mutti, Yaniv Romano et al. |
|---|---|
| Primary category | cs.RO |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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