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arXiv AI By Tianxing Chen, Yuran Wang, Mingleyang Li, Yan Qin, Hao Shi, Zixuan Li, Yifan Hu, Yingsheng Zhang, Kaixuan Wang, Yue Chen, Hongcheng Wang, Junjie Wang, Tianhang Yang, Renjing Xu, Ruihai Wu, Yao Mu, Yaodong Yang, Hao Dong, Ping Luo

RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design

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RMBench is a simulation benchmark featuring nine robotic manipulation tasks that vary in memory complexity, designed to systematically evaluate how well policies use memory. The authors also introduce Mem-0, a modular policy with explicit memory components that allows controlled ablation studies. Experiments in simulation and the real world reveal memory-related limitations in current policies and show how architectural choices affect memory performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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