arXiv cs.AI· Soumil Rathi·· 6 天前AI 评分29
RoboMME-Interference：干扰条件下的机器人记忆基准测试
RoboMME-Interference: Benchmarking Robot Memory Under Interference
AI 导读
研究者提出 RoboMME-Interference，一个基于 RoboMME 的跨会话基准，通过在查询演示后叠加受控数量的无关会话，衡量 VLA 在长上下文干扰下的记忆表现。测试显示，感知记忆变体在无干扰时提升成功率，但随着无关会话累积会持续明显衰减；子目标变体提升较小但更抗干扰。为最强感知变体加入检索步骤后，可在各干扰水平恢复其无干扰成功率。
正文 · 原文
Abstract:Robots deployed in realistic settings will accumulate experience across many sessions and tasks over their deployment. The robot's tasks may often require it to remember information from multiple sessions ago, making long-context robot memory important for real-world deployments. However, most robot-memory benchmarks today are based on single episodes or a short context. To measure how current robot memory systems perform on longer sessions with more distractions, we introduce RoboMME-Interference, a cross-session benchmark built on RoboMME (Dai et al., 2026). For each query episode, we construct a session history using the query's relevant prior demonstration followed by a controlled number of unrelated sessions, which we provide to the VLA as memory and measure accuracy. Running RoboMME's released memory-augmented $\pi_{0.5}$ 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. The subgoal variants, which read the history with a vision-language model and pass written subgoals to the policy, improve less at their best but hold more of that improvement as distractors accumulate. Adding a retrieval step to the strongest perceptual variant, which selects the section of history most visually similar to the robot's current view and passes only that section to the policy, restores its no-distractor success rate at every interference level. With this release, we emphasize the importance of long-context memory and robustness to interference and show that current systems largely fail on such capabilities. The project page, videos, code, and data are at this https URL.
| Comments: | 9 pages, 5 figures. Updated results; added subgoal-memory systems |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.22338 [cs.RO] |
| (or arXiv:2606.22338v4 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2606.22338 arXiv-issued DOI via DataCite |
Submission history
From: Soumil Rathi [view email]
[v1]
Sun, 21 Jun 2026 05:06:16 UTC (54 KB)
[v2]
Tue, 28 Jul 2026 00:23:57 UTC (62 KB)
[v3]
Wed, 26 Aug 2026 09:52:30 UTC (72 KB)
[v4]
Fri, 9 Oct 2026 16:43:07 UTC (68 KB)
来源：arXiv cs.AI · arxiv.org