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Zhejiang University
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APT achieves substantial improvements in instruction generalization for VLA models by effectively decoupling language and action learning, addressing a critical data imbalance issue.
LadderMan enables humanoid robots to climb ladders and manipulate objects with unprecedented robustness and adaptability in real-world scenarios.
Agent-generated feedback not only enhances report quality but also improves task performance and knowledge transfer in crowdsourced testing environments.
By enabling robots to learn from synthesized data in novel contexts without additional motion data, RoboDream transforms the landscape of robot learning efficiency.
