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arXiv Machine Learning By Ralf R\"omer, Maximilian Seeliger, Saida Liu, Ben Sturgis, Marco Bagatella, Daniel Marta, Andreas Krause, Angela P. Schoellig

Uncertainty Quantification for Flow-Based Generalist Robot Policies

Read the original on arXiv Machine Learning →

The paper introduces a method for quantifying epistemic uncertainty in flow‑matching based generalist robot policies, such as vision‑language‑action models and world‑action models. By measuring velocity‑field disagreement across a small ensemble, the authors obtain better‑calibrated uncertainty estimates that can detect deployment failures and guide active fine‑tuning. Their SAVE approach reduces the need for expert demonstrations, improving real‑world task success from 39 % to 47 % while maintaining a fixed demonstration budget.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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