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Simulation-based pre-training can drastically improve the dexterity of robotic hands, outperforming traditional training methods with just a fraction of real-world data.
The ABC framework empowers researchers with the largest open-source teleoperation dataset and a complete toolkit to accelerate advancements in behavior cloning for robotic manipulation.
Achieving long-range consistency in video generation is now feasible with a hierarchical token approach that balances detail and computational efficiency.
Supervised Memory Training enables RNNs to learn long-range dependencies more effectively while training in parallel, outperforming traditional methods.
