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About this paper
Yaru Niu, Binghong Chen, Changwei Yao, Shangtao Li, Zhenlong Fang, Revanth Krishna Senthilkumaran, Shuai Zhou, Yuemin Mao and 14 more
EgoX enables manipulation policies to learn from human, robot, and simulated demonstrations while keeping each embodiment’s sensing and control interfaces distinct. Its modular Transformer shares corresponding signals and predicts future latent representations during training. Across 13 task–robot pairs, pretraining raises normalized task progress from 0.63 to 0.80, including OOD transfer without establishing generalization beyond all training data.
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