UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies
Modern robot learning systems increasingly rely on dense progress or value signals to evaluate intermediate states, guide policy learning, and detect task completion, making the quality of these signals critical. Since such dense labels are rarely available at scale, normalized time within a demonstration is often used as a scalable substitute: later frames are treated as higher progress. However, this time-derived label is only a noisy proxy for physical task progress. In contact-rich manipulat
Record details
Published: 14 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation · Agents & autonomy · Finance, VC & PE
Retrieved: 15 July 2026
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ethics.ai (14 July 2026), “UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies,” evidence record 10453, https://ethics.ai/record/10453 (originally published by arXiv cs.AI).
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