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arXiv Computer Vision By Wenxuan Guo, Yingping Liang, Zhaoguo Wang, Ziyuan Li, Angyuan Ma, Zhenyu Wu, Chengkun Wang, Bingyao Yu, Ze Chen, Borui Zhang, Xiuwei Xu, Meng Zhang, Erjin Zhou, Jiwen Lu

LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts

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LoopWAM is a closed‑loop world‑action model that learns from the full spectrum of real‑world rollouts—successes, suboptimal actions, and failures—by combining behavior learning with action‑conditioned world modeling. It uses source‑dependent noise sampling, quality steering, and quality‑weighted supervision to retain video supervision across all quality levels while regulating action imitation. On several robotics benchmarks, including RoboChallenge Table30‑V2, RoboTwin 2.0, RoboCasa365, and a shirt‑folding task, LoopWAM achieves significant performance gains after a few deployment‑and‑training rounds, outperforming comparable methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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