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About this paper
Yu Liu, Hetian Guo, Tianlv Huang, Ziyi Cai, Wudi Chen, Hantang Wang, Qiutong Liu, Yingzhi Peng and 6 more
PLaW-VLA conditions robot action generation on forecasts of task-relevant future states, rather than reconstructed future images. Its world model predicts latent states from observation history and task semantics, which an action expert uses to generate actions. On RoboTwin Hard Horizon III, it reaches 79.2% success versus π0.5’s 67.4%; across 50 tasks, it improves on 36, ties 4, and declines on 10.
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