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Achieving a 7.4x boost in zero-shot generalization for drone racing without sacrificing speed could redefine performance benchmarks in RL applications.
Robots can recover from dynamic disturbances five times faster than traditional methods, revolutionizing real-time policy adaptation.
Train high-speed, event-camera-guided quadrotors without expensive event rendering by decoupling representation learning from policy learning.
