Uncertainty Quantification for Flow-Based Generalist Robot Policies
Generalist robot policies, such as vision-language-action models (VLAs) and world-action models (WAMs), combine powerful pretrained backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets. Despite their strong empirical performance in robotic manipulation, these policies lack mechanisms to quantify confidence in their predictions and to detect when their actions may be unreliable. This presents a critical limitation for real-world deployment in non-stationary environments, where models inevitably encounter scenarios outside their pretraining distribution and may fail without warning. To address this, we derive an efficient method to quan...
| Authors | Ralf Römer, Maximilian Seeliger, Saida Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 16 June 2026 |
| Last updated at source | 2 October 2026 |
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