arXiv Computer Vision
Identifying Habit, Physics, and Nuisance in Robot World Models
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Persistent Robot World Models: Stabilizing Multi-Step Rollouts via Reinforcement Learning
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CSWAM: Better Causal Semantic Representations for Out-of-Distribution Generalization in World Action Models
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Foresight Without Seeing: Latent Futures for World Action Models
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The Imitator Game: Benchmarking Robot Imitative Ability Beyond Action Prediction
arXiv:2608.22301v1 Announce Type: cross Abstract: Humans imitate at the level of intent: given a demonstration, we infer its goal and carry it out with whatever tools, objects, and layouts are at han...
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Retiming Robot Demonstrations with a Learned Speed Prior
The paper introduces a learned speed prior that predicts a distribution over execution speed for robot demonstrations, allowing consistent retiming across tasks. By applying this prior to both robot teleoperation and handheld demonstrations, the authors retime demonstrations to a handheld pace before fine‑tuning a vision‑language‑action policy. Experiments on 120 RoboLab tasks and a real robot show that policies trained with the learned clocks achieve higher success rates and smoother motion, outperforming policies fine‑tuned on original or uniformly accelerated timings.