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Revealing robot motion in video models can transform how we predict and control robotic actions, achieving high fidelity with minimal training data.
Scaling visuomotor context to 8K timesteps enables robots to master complex tasks and adapt in real-time, outperforming previous models by a staggering margin.
Transforming video generation from a pixel sampling problem to a structured orchestration of the physical world, WNM enables unprecedented control and efficiency in content creation.
Tactile-reactive policies can boost robotic manipulation success rates by over 30% through innovative data collection and a new Mixture-of-Transformers architecture.
Robots can now learn to reproduce oil paintings with impressive accuracy through self-play and model-based planning, even without human demonstrations or high-fidelity simulators.
