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About this paper
Rongxing Ding, Hongyu Qu, Kehong Wu, Xudong Wang, Fan Zhang, Xinguang Xiang, Xiangbo Shu
HiFOR predicts short-term states, intermediate milestones, and final goals for robot manipulation. It organizes these futures by horizon, camera view, and spatial region, then uses the evolving action proposal to retrieve relevant features during action generation. In RoboTwin 2.0, adding action-guided retrieval raises average success from 46.95% to 49.40%, an ablation result that does not compare alternative retrieval designs.
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