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Ditch slow, iterative ODE solvers for robot control: this method distills flow-based policies into a single-step model that's fast enough for real-time replanning without sacrificing multi-modal action diversity.
A robot can now achieve 90% success in peg-in-hole tasks, even with only 0.1mm clearance, by intelligently fusing vision and tactile feedback when visual occlusion occurs.
Humanoid robots can now perform complex loco-manipulation tasks with more natural and stable movements by decomposing control into VLM-orchestrated expert policies trained with human motion priors.
