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Unlock bimanual robot skills without bimanual robots: MonoDuo lets you train bimanual policies using only a single-arm robot and a human partner.
Tactile sim-to-real just got real: a physics-grounded representation unlocks zero-shot transfer for complex dexterous manipulation tasks, even without ground truth sensor calibration.
Forget bounding boxes – predicting the future of animal movement in the wild is now possible with interpretable trajectory tokens and a diffusion transformer.
Current AI's hunger for curated data may be solved by a new architecture inspired by human cognition that flexibly switches between observation, active behavior, and meta-control.
Forget tactile simulation: PTLD distills real-world tactile sensor data into a robust state estimator that supercharges sim-trained manipulation policies.
Robots can now peel cucumbers, apples, and potatoes with 90% success by learning from a small number of human preferences, even generalizing to new produce types.
Forget tedious manual calibration: D-REX automatically builds high-fidelity digital twins by identifying object mass directly from real-world grasping data.
Human-level 3D perception can emerge from a surprisingly simple, scalable learning objective using multi-view images, finally closing the gap between AI and human performance on this fundamental visual task.
Forget clunky skeletons: this new model lets you prompt your way to accurate 3D human meshes from single images, even in the wildest poses.
