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Policies may succeed in tasks but still violate deformation tolerances, revealing a critical gap in current evaluation methods for deformable-object manipulation.
Zero-robot post-training on high-fidelity UMI data achieves real-world performance that rivals traditional teleoperation methods, revolutionizing data efficiency in robotic manipulation.
Human demonstrations can yield over 10x the recovery data for robots, dramatically enhancing their ability to recover from failures in real-world tasks.
Despite high success rates for isolated skills, robots frequently stall in long-horizon tasks due to semantic handoff failures, revealing a hidden challenge in skill composition.
