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Domain randomization doesn't just make your robot policies more robust; it fundamentally warps the optimization landscape, potentially guiding your search towards better contact-rich behaviors.
Guaranteeing stability for complex robot locomotion just got easier: HALO learns low-dimensional models that accurately predict stability regions in the full state space.
Real-world flight tests show control barrier functions can effectively constrain a human pilot's inputs on an F-16, enforcing safety limits without overly restricting maneuverability.
Legged robots can now navigate complex environments with context-aware safety margins, thanks to a new framework that understands object semantics and adjusts safety protocols accordingly.
Humanoid robots can now walk more robustly on uneven terrain thanks to a hierarchical MPC approach that cleverly incorporates arm and torso dynamics for improved stability.
