Step Forcing lets a 4-step video world model generate 30-second closed-loop rollouts, and a VLM judge scores them to reproduce the real RoboArena policy ranking at r=0.989.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
CONDITIONAL 2representative citing papers
Projecting robot end-effector state onto image feature maps and sampling co-located visual tokens improves manipulation policy success by 4-10% across 67 tasks.
citing papers explorer
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RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation
Step Forcing lets a 4-step video world model generate 30-second closed-loop rollouts, and a VLM judge scores them to reproduce the real RoboArena policy ranking at r=0.989.
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GeoProp: Grounding Robot State in Vision for Generalist Manipulation
Projecting robot end-effector state onto image feature maps and sampling co-located visual tokens improves manipulation policy success by 4-10% across 67 tasks.