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Action-conditioned objectives can significantly enhance the effectiveness of Euclidean-cost MPC by aligning latent representations with real task progress.
Zero-robot post-training on high-fidelity UMI data achieves real-world performance that rivals traditional teleoperation methods, revolutionizing data efficiency in robotic manipulation.
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.
