NVIDIA 开源 Industrial ReMEmbR：给仓库机器人集群装上时空记忆系统
Industrial ReMEmbR: Scaling Spatiotemporal Memory to Robot Fleets
NVIDIA 把 ReMEmbR 扩到机器人集群了，Jetson 本地编码不上传原始画面，还能抹掉人脸过 GDPR，做仓储机器人的可以看看。
Industrial ReMEmbR 把 NVIDIA ReMEmbR 扩展到机器人集群，构建共享时空记忆系统。每台机器人上的 NVIDIA Jetson 用 Cosmos-Embed1、Cosmos3-Edge 和 Wan2.2 VAE 在本地编码视频，不上传原始画面，并对检测到的人像做抹除以符合 GDPR。Cosmos3-Edge 在视觉-语言边界被拆分：视觉编码器留在机器人端，语言解码器放在本地服务器，由 Nemotron 智能体按内容、位置和时间检索记忆。该设计省去了写入时的场景描述生成，450 次解码测试中缓存 token 解码输出与未拆分模型完全一致。
Industrial ReMEmbR: Scaling Spatiotemporal Memory to Robot Fleets
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.
