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 manipulation, a robot may make progress and then lose it through slips, failed grasps, or partial undoing, wh
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- PossiblePossibly related (embedding) · 51%UK startup Humanoid launches reinforcement learning system to improve robot manipulation - Robotics & Automation News →
- PossiblePossibly related (embedding) · 46%Gradient-based Planning for World Models at Longer Horizons →
- LinkedLinked via arxiv author · 85%Lirui Zhao →
“UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies”
- LinkedLinked via arxiv author · 85%Modi Shi →
“UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies”
- LinkedLinked via arxiv author · 85%Li Chen →
“UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies”
- LinkedLinked via arxiv author · 85%Jiaqi Liu →
“UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies”
- LinkedLinked via arxiv author · 85%Ping Luo →
“UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies”
- LinkedLinked via arxiv author · 85%Hongyang Li →
“UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies”