Task Planning for Mobile Manipulation in Retail Stores using Foundation Models with Iterative Re-planning

Published July 14, 2026 · Category: Robotics

Overview

arXiv:2607.09962v1 Announce Type: new Abstract: Automation in industries such as retail, warehousing and logistics presents opportunities for greater throughput, cost reduction and mitigation of disruptions from labour shortages. Previously, such efforts have focused on back-room operations involving packing and sorting in relatively structured environments. With advances in robotic mobile manipulation hardware and foundation models, automation can now be applied to more variable and human-centric environments such as retail store shelves. In this work, we present a task-planning approach using Large Language Models (LLMs) and Vision-Language Models (VLMs) to address the restocking problem in retail scenarios such as supermarkets. We demonstrate this system on a custom omnidirectional mobile manipulation platform, with user-driven prompts and a feedback-based iterative re-planning approach for error correction. The end-to-end system is validated in a PyBullet simulation environment for pick-and-place tasks.

Source

Originally published at arxiv.org.

Related Articles

• Performance Characterization of Frequency-Selective Wireless Power Transfer Toward Scalable Untethered Magnetic Actuation
• Casting Everything to Online API Services? A Survey of Integrating Localized Speech Recognition Models in Robotic Systems
• ReMoSPLAT: Reactive Mobile Manipulation Control on a Gaussian Splat