RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design
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 ma...
| Authors | Tianxing Chen, Yuran Wang, Mingleyang Li et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 March 2026 |
| Last updated at source | 15 October 2026 |
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