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TTP enables robots to learn dexterous manipulation from human tactile experiences, achieving unprecedented performance in complex tasks.
Achieving an 88.75% success rate in dexterous manipulation tasks, RealDexUMI bridges the gap between human demonstrations and robot execution without losing critical dexterity.
Forget robot-specific fine-tuning: a unified diffusion model can now learn policies across diverse robot embodiments, boosting performance by 15% and opening doors to truly generalizable robotic agents.
Diffusion models can now plan effectively for long-horizon tasks by strategically generating subgoals that are then efficiently realized by rectified flow models.
