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About this paper
Zhongxi Chen, Yi Yang, Xinyi Chen, Mingzhe Ye, Yaping Li, Zhihong Liu, Congsheng Xu, Haonan Wang and 1 more
DexForesight transfers action-conditioned hand–scene dynamics learned from depth, robot kinematics, and demonstrations into policy features, so deployment uses current RGB, language, and robot state without depth or world-model execution. On 11 DexJoCo simulation tasks, it achieves 60.5% overall success, versus 49.4% for GR00T N1.7; on three real-robot tasks, it reaches 58.3%, with comparisons limited by differing training and architectures.
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