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LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts

Paper recorded by Signals 4 on 2026-10-13 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-13 on arXiv · recorded by Signals 4 on 2026-10-14

Category: cs.CV · 计算机视觉 · first seen 2026-10-14

Abstract

Scaling expert demonstrations alone does not enable generalist robot policies to improve from deployment. Real-world rollouts contain successes, suboptimal behavior, and failures: imitating every recorded action can reinforce poor behavior, while filtering out failures discards evidence of physical interactions and their consequences. We introduce \textbf{LoopWAM}, a closed-loop world-action model

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#277 most recent of 756 cs.CV papers we have recorded · ↑ newer: Quantifying privacy risks in foundation model representations of brain · ↓ older: EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipula
Cite this page: LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts: the #277 most recent of 756 cs.CV papers we have recorded (as of 2026-10-13). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/loopwam-closed-loop-generalist-policy-improvement-from-deployment-rollouts.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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