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Current action-conditioned world models are limited by their reliance on visual patterns, failing to generalize physical dynamics across different robot embodiments.
FlowWAM achieves a remarkable 92.94% success rate in manipulation tasks by harnessing optical flow as a video-native action representation.
E-TTS achieves up to a 33.14% performance boost in robotic manipulation by leveraging historical context and iterative refinement, redefining how we approach test-time scaling.
Forget verbose instructions: this new VLN paradigm uses floor plans to guide navigation with concise commands, boosting success rates by 60%.
