Jad Yammine, Malek Wahidi, Abrar Anwar, Yan Chang, Mihir Acharya, Anthony Rizk · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28624 · open-access PDF
창고 로봇 함대는 지속적으로 작동 조건을 관찰하지만, 이러한 관찰을 재사용 가능한 상징적 메모리로 보존하면 상당한 계산, 통신 및 저장 비용이 발생합니다. 예를 들어, 반복적 인 통로 차단을 설명하는 것은 여러 차례에 걸쳐 여러 로봇에서 비디오를 검토해야 할 수 있습니다. 우리는 NVIDIA ReMEmbR를 로봇 함대에 확장하고 서버 측면 컴퓨팅을 줄이는 공유 공간 시간 메모리 시스템 인 IndustrialRemembr을 소개합니다.
Machine translation (Meta M2M100); the English original follows.
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 Co
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