arXiv cs.AI· Jad Yammine, Malek Wahidi, Abrar Anwar, Yan Chang, Mihir Acharya, Anthony Rizk·· 13 小时前AI 评分38
Industrial ReMEmbR：将时空记忆扩展至机器人集群
Industrial ReMEmbR: Scaling Spatiotemporal Memory to Robot Fleets
AI 导读
IndustrialRemembr 将 NVIDIA ReMEmbR 扩展为面向机器人集群的共享时空记忆系统，通过仅在需要时生成场景描述来降低服务端计算开销。每台机器人用 NVIDIA Jetson 上的 Cosmos-Embed1 编码检索嵌入、Cosmos3-Edge 提取视觉 token、Wan2.2 VAE 生成重建潜变量，无需上传原始摄像头画面。
正文 · 原文
Abstract:Warehouse robot fleets continuously observe operational conditions, but preserving these observations as reusable semantic memory incurs substantial computation, communication, and storage costs. For example, explaining recurring aisle blockages can require reviewing video from multiple robots across several shifts. We present IndustrialRemembr, a shared spatiotemporal memory system that extends NVIDIA ReMEmbR to robot fleets and reduces server-side computation by generating scene descriptions only on demand. On each robot, an NVIDIA Jetson encodes video clips into retrieval embeddings with Cosmos-Embed1, vision tokens with Cosmos3-Edge, and reconstruction latents with the Wan2.2 VAE, without uploading raw camera frames. The encoding models are re-used to detect people in image patches, zeroing them out to support PII abstraction for GDPR-compliant deployments. We split Cosmos3-Edge at its vision-language boundary, placing the vision encoder on the robot and the language decoder on an on-premises server. A Nemotron agent retrieves memories by content, position, and time and queries the decoder using cached vision tokens. This design eliminates caption generation at write time, while cached-token decoding reproduces the unsplit model's output in all 450 tested decodes.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) |
| Cite as: | arXiv:2610.28624 [cs.RO] |
| (or arXiv:2610.28624v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28624 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jad Yammine [view email]
[v1]
Tue, 20 Oct 2026 17:58:32 UTC (6,542 KB)
来源：arXiv cs.AI · arxiv.org