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Tactile feedback from WT-UMI enables humanoid robots to achieve superior manipulation performance by effectively bridging the gap between human intuition and robotic execution.
Weak-to-strong reward models can ace the test but still fail in the real world, revealing a hidden brittleness in current preference learning approaches.
