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

TL;DR

RMBench is introduced, a simulation benchmark comprising 9 manipulation tasks that span multiple levels of memory complexity, enabling systematic evaluation of policy memory capabilities and Mem-0, a modular manipulation policy with explicit memory components designed to support controlled ablation studies.

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 designed to support controlled ablation studies. Through extensive simulation and real-world experiments, we identify memory-related limitations in existing policies and provide empirical insights into how architectural design choices influence memory performance. The website is available at https://rmbench.github.io/.

Figures (11)

• Figure 1: RMBench Tasks. We illustrate the nine memory-dependent tasks in RMBench along with their key execution steps. Tasks detailed description are shown in Appendix. \ref{['sec:benchmark_description']}.
• Figure 2: Mem-0 Pipeline. Mem-0 comprises a Planning Module and an Execution Module linked by a Subtask End Classifier. The Planning Module generates high-level subtasks from task instructions, observations, and key-frame memory, while the Execution Module produces low-level actions using the current observation, the subtask, and fused anchor and sliding memories in a diffusion-based policy. Upon subtask completion, a key frame is stored to enable iterative planning and execution until task completion.
• Figure 3: Visualization of Baseline Typical Error. Because the baseline predicts the next action solely from the current observation, it struggles to perform reliably on non-Markovian tasks that require persistent memory over time.
• Figure 4: Real-world Experiment Tasks. The real-world experimental setup is illustrated above.
• Figure 5: Failure examples of Observe and Pick Up. (Top) Confused by objects with similar colors and shapes. (Middle) Confused by identical object morphologies. (Bottom) General failure to identify the target, resulting in the robot grasping a mean position or unintended position.