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About this paper
Wenzhan Li, Yiran Qin, Heng Zhou, Huirui Wang, Yulan Guo, Ruimao Zhang
MERL uses reliability estimates to control imagined experience during robot policy refinement. At the stage level, simulator error sets the amount of imagined data and rollout length; within a stage, chunk reliability guides replay and update weights. On perturbed LIBERO-PRO, it reports 79.7% average success versus 77.2% for grounded-only refinement, with fewer stages to threshold; this does not establish broad new-task learning.
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