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About this paper
Yixuan Yang, Qiang Chen, Run Shao, Shuwei Dong, Wenhao Li, Yunpeng Wang, Wenhan Fang, Haomiao Wu and 26 more
The authors introduce a robot-policy improvement loop that diagnoses the earliest manipulation stage a policy cannot reliably pass, then uses that objective to guide offline learning from curated experience. Candidate updates are interpolated with the inherited policy and evaluated across RoboDojo-Sim. CSU-AI-07 scores 26.15 and 20.70% success, but this benchmark does not isolate component effects or establish real-world performance.
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