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This paper presents CosmoH2G, a dataset and baseline method for transferring complex human hand demonstrations to robotic grippers using a two-stage framework that predicts sparse keyframes and continuous actions to handle intricate spatial movements.
HOTFIXR is a data generation framework that targets multilingual reasoning weaknesses in LLMs to improve cross-lingual performance using synthetic data without sacrificing overall capability.