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cs.RO2026
GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation
AgiBot Research Team, Renhang Liu, Wenzhi Zhao +42
World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled interaction. Most inherit pretrained video…
cs.RO2026
Efficient Camera Pose Augmentation for View Generalization in Robotic Policy Learning
Sen Wang, Huaiyi Dong, Jingyi Tian +7
Prevailing 2D-centric visuomotor policies exhibit a pronounced deficiency in novel view generalization, as their reliance on static observations hinders consistent action mapping a…