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Forget static coordination – robots that chat and dynamically re-plan can achieve a whopping 69% improvement in collaborative navigation success.
Humanoid robots can now handle heavy, unknown payloads in the real world thanks to a system that identifies mass distribution via differentiable simulation.
Robots can now learn fine-grained manipulation skills directly from human demonstrations, thanks to a new imitation learning framework that automatically figures out the right rewards and handles noisy real-world data.
Forget brittle, single-skill robots: X-Loco achieves robust, vision-based humanoid locomotion by intelligently combining multiple expert policies for fall recovery, terrain traversal, and whole-body coordination.
Humanoid robots can now reliably perform long-horizon loco-manipulation tasks in the real world thanks to a novel root-trajectory conditioned policy and persistent object estimation.
LLMs can learn better from human feedback by exploring more creatively, thanks to a simple coin-flip counting method that encourages them to try new things.
