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Standard LLMs can now perform complex bimanual robot manipulation tasks with impressive success rates, all without any task-specific training.
Surprisingly, even a mediocre whole-body controller can be leveraged with offline RL to achieve impressive mobile manipulation, rivaling hand-tuned controllers and generalizing to the real world without any real-world training.
Forget expensive real-world data collection: a massive, diverse synthetic dataset enables surprisingly effective zero-shot transfer for robotic manipulation.
Robots can now achieve superior surface coverage with precise end-effector poses thanks to a new SE(3)-aware Stein Variational Gradient Descent method that outperforms existing trajectory optimization techniques.
By distilling a frozen diffusion model's geometric understanding into a fast, deterministic network, Robot-DIFT unlocks more precise robot control compared to standard vision encoders.
Forget synthetic benchmarks that don't translate: MolmoSpaces offers 230k diverse, simulator-agnostic environments with 130k annotated objects, showing a remarkable 0.96 sim-to-real correlation for robot policies.
