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CoRe achieves preference-aligned reinforcement learning by seamlessly integrating human-like reward decomposition, outperforming traditional methods in both simulated and real-world environments.
Tactile robotic perception gets a boost with a new pretraining method that explicitly encodes force, geometry, and orientation, leading to a 52% reduction in regression error.
