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Industrial ReMEmbR: Scaling Spatiotemporal Memory to Robot Fleets

Paper recorded by Signals 4 on 2026-10-20 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-20 on arXiv · recorded by Signals 4 on 2026-10-21

Category: cs.AI · 人工智能 · first seen 2026-10-21

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 N

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#224 most recent of 960 cs.AI papers we have recorded · ↑ newer: UniWALM: Unified World Action Language Model for Embodied Robot Contro · ↓ older: MindView: Benchmarking Generative Multi-View Spatial Intelligence
Cite this page: Industrial ReMEmbR: Scaling Spatiotemporal Memory to Robot Fleets: the #224 most recent of 960 cs.AI papers we have recorded (as of 2026-10-20). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/industrial-remembr-scaling-spatiotemporal-memory-to-robot-fleets.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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