Splitting large vision-language models at their architecture boundary—vision encoder on robots, language decoder on servers—enables efficient fleet-scale memory systems that generate descriptions on-demand rather than at write time, cutting server costs while preserving retrieval quality.
IndustrialRemembr extends a spatiotemporal memory system to robot fleets by splitting vision-language models between edge devices and servers. Robots encode video into embeddings and tokens without uploading raw frames, while an AI agent retrieves memories on demand and generates descriptions only when needed, reducing computation and storage costs while maintaining GDPR compliance.