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[2602.22474] When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering
Summary
This article presents a framework for uncertainty-aware policy steering in robotics, enabling adaptive robot behavior by addressing task and action uncertainties during deployment.
Why It Matters
As robotics technology advances, the ability to adaptively steer robot behavior in uncertain environments is crucial for effective deployment. This framework enhances the reliability of robotic systems by integrating uncertainty management, which can lead to improved performance and reduced need for human intervention.
Key Takeaways
- The framework introduces uncertainty-aware policy steering (UPS) for adaptive robot behavior.
- It addresses both high-level task ambiguities and low-level action uncertainties.
- The method leverages conformal prediction to enhance decision-making reliability.
- Continual learning is facilitated with minimal human feedback through residual learning.
- Experiments demonstrate UPS's effectiveness in reducing user interventions compared to traditional methods.
Computer Science > Robotics arXiv:2602.22474 (cs) [Submitted on 25 Feb 2026] Title:When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering Authors:Jessie Yuan, Yilin Wu, Andrea Bajcsy View a PDF of the paper titled When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering, by Jessie Yuan and 2 other authors View PDF HTML (experimental) Abstract: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. In practice, the overconfident judgment from VLM can degrade the steering performance under both high-level semantic uncertainty in task specifications and low-level action uncertainty or incapability of the pre-trained policy. We propose uncertainty-aware policy steering (UPS), a framework that jointly reasons about semantic task uncertainty and low-level action feasibility, and selects an uncertainty resolution strategy: execute a high-confidence action, clarify task ambiguity via natural language queries, or ask for action interventions to correct the low-level policy when it is deemed incapable at the task. We leverage conformal prediction to calibrate the composition of the VLM and the pre-trained base policy...
