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2026-02-26 09:45:39
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RoboDreamer: Learning Compositional World Models for Robot Imagination R o b o D r e a m e r Learning Compositional World Models for Robot Imagination Siyuan Zhou 1 , Yilun Du 2 , Jiaben Chen 3 , Yandong Li 4 , Dit-Yan Yeung 1 , Chuang Gan 5,6 1 HKUST , 2 MIT , 3 UCSD , 4 UCF , 5 UMass Amherst , 6 MIT-IBM Watson AI Lab ICML 2024 arXiv Code Abstract Text-to-video models have demonstrated substantial potential in robotic decision-making, enabling the imagination of realistic plans of future actions as well as accurate environment simulation. However, one major issue in such models is generalization -- models are limited to synthesizing videos subject to language instructions similar to those seen at training time. This is heavily limiting in decision-making, where we seek a powerful world model to synthesize plans of unseen combinations of objects and actions in order to solve previously unseen tasks in new environments...
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