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About this paper
Xingyu Wang, Yanjiang Guo, Zhijing Ding, Jiaxin Fang, Zeyu Lai, Jiayu Dong, Weikang Wan, Jianyu Chen and 2 more
HiLo-WAM separates visual prediction into sparse task-history and coarse-future streams and dense local-interaction streams, then uses both with robot state and instructions to generate action chunks. On RoboTwin 2.0, it reports 93.70% average success across 50 bimanual tasks, versus Fast-WAM’s 91.85%. This benchmark comparison does not establish causation; the model remains dependent on video-model understanding and prediction.
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