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The Robot Data Factory is introduced, a mission-driven infrastructure and methodology for continuously generating, validating, benchmarking, and reusing robot experience that reframes robot data generation as a continuous scientific production process and provides a pathway toward reproducible, scalable, and eventually federated infrastructure for Physical AI.
Articulated 3D object reconstruction can now achieve high fidelity and internal structure recovery from mere text or images, thanks to a debate-driven agentic approach.
Today's best LLMs fail spectacularly at long-horizon reasoning, achieving under 10% accuracy on a new benchmark designed to isolate this critical capability.
Achieve state-of-the-art robot manipulation success rates while slashing inference costs by up to 72% with A1, a fully open-source VLA framework that adaptively truncates computation.
Current robot manipulation benchmarks fail to capture the messy reality of real-world deployment, so this work introduces a new benchmark, ManipArena, to close the sim2real gap.
Reconstructing realistic 3D scenes with accurate object contacts is now possible thanks to the new MessyKitchens dataset and the Multi-Object Decoder (MOD).
