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About this paper
Chenghao Yue, Peiyan Li, Hao Wang, Lei Lv, Jiahao Chen, Huaping Liu, Yu Luo, Yuanlong Zhang
PRISM changes training for vision-language-action robot policies using an offline precision-demand signal from demonstrations to emphasize consequential action samples and train visual-language representations. It also weights action errors by flow-time visibility while leaving inference unchanged. Across three real-robot tasks, mean success rose from 38.3% to 86.7%, based on 20 trials per task and supporting gains on these tasks only.
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