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About this paper
Huabin Peng, Fagang Jin, Yancong Wei, Tengfei Ma, Chuhang Liu, Chao Yu, Liqing Chen
PRIME W0 adds temporal context to a vision-language-action robot policy by combining a predicted visual subgoal from the previous observation, lightweight features from recent frames, and an action generator conditioned on representations from every VLM layer. Its mean task-success rate across four LIBERO suites is 99.1%, versus 98.4% for EO-1; it does not lead every suite.
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