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RynnBrain 1.1 not only outperforms all competitors in embodied cognition tasks but also redefines how robots can be trained for complex manipulation through innovative 3D grounding techniques.
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.
Latent spatial memory can accelerate video generation by over 10 times while dramatically reducing memory usage, revolutionizing how we model dynamic scenes.
Forget manual camera trajectories: CT-1 learns to generate them automatically, bridging the gap between spatial reasoning and high-quality video synthesis.
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.
