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About this paper
Min Lin, Bokai Zhou, Jianqi Lin, Kaiyuan Yang, Yicheng Chang, Junjie Yu, Xiaofan Liu, Jianhua Han and 3 more
AFCE trains a visuomotor policy using temporally ordered Effects built from actions, robot responses, and visual transitions. An offline tokenizer constructs these training targets, while the deployed policy predicts them from current images, instructions, and robot state, then decodes them into actions. On simulated DexJoCo, AFCE achieves 50.97% overall success and improves bimanual results, but remains weak on precision control.
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