RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies

• URL: http://arxiv.org/abs/2603.04639v1
• Date: Wed, 04 Mar 2026 21:59:32 GMT
• Title: RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies
• Abstract summary: Memory is critical for long-horizon and history-dependent robotic manipulation.<n>Recent vision-language-action (VLA) models have begun to incorporate memory mechanisms.<n>We introduce RoboMME: a large-scale standardized benchmark for evaluating and advancing VLA models.
• Score: 54.23445842621374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Memory is critical for long-horizon and history-dependent robotic manipulation. Such tasks often involve counting repeated actions or manipulating objects that become temporarily occluded. Recent vision-language-action (VLA) models have begun to incorporate memory mechanisms; however, their evaluations remain confined to narrow, non-standardized settings. This limits their systematic understanding, comparison, and progress measurement. To address these challenges, we introduce RoboMME: a large-scale standardized benchmark for evaluating and advancing VLA models in long-horizon, history-dependent scenarios. Our benchmark comprises 16 manipulation tasks constructed under a carefully designed taxonomy that evaluates temporal, spatial, object, and procedural memory. We further develop a suite of 14 memory-augmented VLA variants built on the π0.5 backbone to systematically explore different memory representations across multiple integration strategies. Experimental results show that the effectiveness of memory representations is highly task-dependent, with each design offering distinct advantages and limitations across different tasks. Videos and code can be found at our website https://robomme.github.io.

Related papers

• Benchmarking Robot Memory Under Interference [0.0]
  We introduce RoboMME-Interference, a cross-session benchmark built on RoboMME.<n>For each query episode, we construct a session history using the query's relevant prior demonstration followed by a controlled number of unrelated sessions.<n>Running RoboMME's released memory-augmented $_0.5$ variants unmodified through this benchmark, we find that while perceptual memory variants improve success when given the history without any distractors, they decay strongly and steadily as unrelated sessions accumulate.
  arXiv  Detail & Related papers  (2026-06-21T05:06:16Z)
• RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark [49.16825786282095]
  Memory is a critical component of robotic intelligence, as robots must rely on past observations and actions to accomplish long-horizon tasks.<n>Existing robotic memory benchmarks still lack multimodal annotations for memory formation, provide limited task coverage and structural complexity, and remain restricted to simulation without real-world evaluation.<n>We address this gap with RoboMemArena, a large-scale benchmark of 26 tasks, with average trajectory lengths exceeding 1,000 steps per task and 68.9% of subtasks being memory-dependent.
  arXiv  Detail & Related papers  (2026-05-11T17:54:49Z)
• LMEB: Long-horizon Memory Embedding Benchmark [49.57481835614834]
  We introduce the Long-horizon Memory Embedding Benchmark (LMEB), a comprehensive framework that evaluates embedding models' capabilities.<n>LMEB spans 22 datasets and 193 zero-shot retrieval tasks across 4 memory types.<n>We evaluate 15 widely used embedding models, ranging from hundreds of millions to ten billion parameters.
  arXiv  Detail & Related papers  (2026-03-13T02:09:57Z)
• MEM: Multi-Scale Embodied Memory for Vision Language Action Models [73.3883864595845]
  We introduce Multi-Scale Embodied Memory (MEM), an approach for mixed-modal long-horizon memory in robot policies.<n>MEM combines video-based short-horizon memory, compressed via a video encoder, with text-based long-horizon memory.<n>MEM enables robot policies to perform tasks that span up to fifteen minutes, like cleaning up a kitchen, or preparing a grilled cheese sandwich.
  arXiv  Detail & Related papers  (2026-03-04T00:03:02Z)
• RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design [77.30163153176954]
  RMBench is a simulation benchmark comprising 9 manipulation tasks that span multiple levels of memory complexity.<n>Mem-0 is a modular manipulation policy with explicit memory components designed to support controlled ablation studies.<n>We identify memory-related limitations in existing policies and provide empirical insights into how architectural design choices influence memory performance.
  arXiv  Detail & Related papers  (2026-03-01T18:59:59Z)
• EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory [63.84216832544323]
  EvolMem is a new benchmark for assessing multi-session memory capabilities of large language models (LLMs) and agent systems.<n>To construct the benchmark, we introduce a hybrid data synthesis framework that consists of topic-initiated generation and narrative-inspired transformations.<n>Extensive evaluation reveals that no LLM consistently outperforms others across all memory dimensions.
  arXiv  Detail & Related papers  (2026-01-07T03:14:42Z)
• MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation [59.31354761628506]
  Temporal context is essential for robotic manipulation because such tasks are inherently non-Markovian, yet mainstream VLA models typically overlook it.<n>We propose MemoryVLA, a Cognition-Memory-Action framework for long-horizon robotic manipulation.<n>We evaluate it on 150+ simulation and real-world tasks across three robots.
  arXiv  Detail & Related papers  (2025-08-26T17:57:16Z)
• FindingDory: A Benchmark to Evaluate Memory in Embodied Agents [49.18498389833308]
  We introduce a new benchmark for long-range embodied tasks in the Habitat simulator.<n>This benchmark evaluates memory-based capabilities across 60 tasks requiring sustained engagement and contextual awareness.
  arXiv  Detail & Related papers  (2025-06-18T17:06:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.