This paper introduces ProWAM, a progressive world action model that predicts actions and sparse visual sub-goals to guide robotic control. It improves long-horizon planning by using a single video-backbone pass for efficient action generation and demonstrates strong performance on simulation and real-world benchmarks.
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ProWAM's approach to predicting sparse visual sub-goals and using a single video-backbone pass can improve efficiency and performance in robotic control tasks.
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