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A unified benchmark that combines synthetic data with real-world testing could revolutionize how we train robots for complex manipulation tasks.
Robot world models can be significantly improved by directly rewarding them for generating videos that lead to physically plausible robot actions, even if the videos themselves contain visual artifacts.
Ditch slow optimization and template retrieval: PAct generates articulated 3D objects from a single image in a feed-forward manner, preserving instance-level detail and articulation plausibility.