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MemoryWAM achieves superior performance in robotic manipulation tasks by efficiently leveraging both short-term and long-term memory without sacrificing computational efficiency.
Forget training separate policies for every robot hand – this method learns one policy to control them all, slashing data needs and boosting performance by 50% in cross-embodiment manipulation.
