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Lucen Robotics
We are building a simulation-first autonomous retail robotics system for night-shift restocking and inventory management. Using NVIDIA Isaac Sim, we model realistic supermarket environments where robots detect out-of-stock shelves, navigate store aisles, and execute pick-and-place restocking tasks autonomously. Our perception pipeline combines computer vision models trained on real supermarket data — for out-of-stock detection and 84.25% SKU classification across 207 products — with depth sensing and SLAM-based navigation within the simulated environment. Robots interact with shelf objects to perform concrete restocking tasks including picking products from backroom inventory, transporting them to target aisles, and placing items in correct shelf positions. The system demonstrates repeatable task execution under varying store layouts and stock conditions, with clear performance metrics and basic failure recovery. A web-based operator dashboard provides real-time monitoring, inventory alerts, and analytics, delivering a complete product experience for retail operators.
15 Feb 2026
