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Decomposing uncertainty into aleatoric and epistemic types lets robots recover from errors 21% more effectively than treating all uncertainty the same.
Robots can recover from unexpected mid-episode failures up to 87% faster by adding a small, carefully constrained control signal on top of a pre-trained policy, without any further training.
Transformer-based visual trackers can slash compute by up to 12% without sacrificing accuracy, simply by dynamically adjusting their depth based on uncertainty.
