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Policies trained on RynnWorld-Teleop's synthetic data achieve zero-shot transfer to real-world tasks, revolutionizing how we collect and utilize robotic training data.
RynnWorld-4D transforms robotic manipulation by co-producing future scene dynamics from a single RGB-D image, leading to unprecedented performance in dexterous tasks.
The medical imaging AI community is being held back by a fragmented data landscape, but a new metadata-driven fusion paradigm offers a path to unlocking the power of foundation models.
Reconstructing articulated 3D objects from casual monocular videos is now possible with Articulat3D, which enforces geometric and motion constraints for geometrically accurate and temporally coherent digital twins.
