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Forget scaling laws – this zero-shot navigation agent beats million-sample trained models by structurally unifying language, vision, and robot actions within the reasoning capabilities of pre-trained MLLMs.
Forget hand-crafted assets and heuristics: V-Dreamer uses video generation models to automatically create diverse, physically plausible robotic simulation environments and trajectories directly from language.
Robots can now adaptively decide whether to clear clutter or directly grasp, leading to significantly improved success rates in densely cluttered environments.
