AI RESEARCH
When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering
arXiv CS.LG
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ArXi:2602.22474v2 Announce Type: replace-cross Policy steering is an emerging way to adapt robot behaviors at deployment-time: a learned verifier analyzes low-level action samples proposed by a pre-trained policy (e.g., diffusion policy) and selects only those aligned with the task. While Vision-Language Models (VLMs) are promising general-purpose verifiers due to their reasoning capabilities, existing frameworks often assume these models are well-calibrated.