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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.
Real-world images plagued by both raindrops and reflections finally get a dedicated benchmark dataset (RDRF) and a diffusion-based model (DiffUR$^3$) that actually works.
Robots can now adaptively decide whether to clear clutter or directly grasp, leading to significantly improved success rates in densely cluttered environments.
