发表机构
TCS Research, Tata Consultancy Services Ltd.（塔塔咨询服务公司TCS研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究零售场景补货问题，利用大语言模型和视觉语言模型，结合定制移动操作平台、用户驱动提示及反馈迭代重新规划方法纠错，在模拟环境验证端到端系统，为零售自动化提供了新的任务规划方案。
AI 中文摘要
零售、仓储和物流等行业的自动化为提高吞吐量、降低成本和缓解劳动力短缺造成的干扰带来了机遇。此前此类努力集中于相对结构化环境中的包装和分拣等后台操作。随着机器人移动操作硬件和基础模型的发展，自动化现在可应用于更具变化性和以人类为中心的环境，如零售商店货架。本文提出一种使用大语言模型（LLMs）和视觉语言模型（VLMs）的任务规划方法来解决超市等零售场景中的补货问题。在定制的全向移动操作平台上展示该系统，采用用户驱动提示和基于反馈的迭代重新规划方法进行纠错。在PyBullet模拟环境中对端到端系统进行了抓取和放置任务的验证。
英文摘要
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.
