[2603.01229] RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design

Computer Science > Robotics arXiv:2603.01229 (cs) [Submitted on 1 Mar 2026] Title:RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design Authors:Tianxing Chen, Yuran Wang, Mingleyang Li, Yan Qin, Hao Shi, Zixuan Li, Yifan Hu, Yingsheng Zhang, Kaixuan Wang, Yue Chen, Hongcheng Wang, Renjing Xu, Ruihai Wu, Yao Mu, Yaodong Yang, Hao Dong, Ping Luo View a PDF of the paper titled RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design, by Tianxing Chen and 16 other authors View PDF HTML (experimental) Abstract:Robotic manipulation policies have made rapid progress in recent years, yet most existing approaches give limited consideration to memory capabilities. Consequently, they struggle to solve tasks that require reasoning over historical observations and maintaining task-relevant information over time, which are common requirements in real-world manipulation scenarios. Although several memory-aware policies have been proposed, systematic evaluation of memory-dependent manipulation remains underexplored, and the relationship between architectural design choices and memory performance is still not well understood. To address this gap, we introduce RMBench, a simulation benchmark comprising 9 manipulation tasks that span multiple levels of memory complexity, enabling systematic evaluation of policy memory capabilities. We further propose Mem-0, a modular manipulation policy with explicit memory components desig...