SBIR Phase I: Advanced Artificial Intelligence for Robotic E-Commerce Pick-and-Pack Automation

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响和商业潜力是促进可靠、灵活和可扩展的物流网络的发展，以分发基本物品和供应品。最近的预测表明，到2022年，美国电子商务总销售额将超过9000亿美元。美国电子商务的自动化处理市场估计为96亿美元。将物品从生产者送到消费者手中的过程涉及许多接触点，在这些接触点上，操作员挑选和包装单个物品。目前，这些过程都是手工的，而且高度重复，造成了很高的伤害率。这些过程中的错误对电子商务提供商来说代价高昂，并可能导致关键物资丢失或严重延误。然而，由于仓库流程的显著多样性，自动化拣选和包装一直具有挑战性，并且需要新的人工智能（AI）机器人控制系统，可以操纵仓库中的大量独特物品。用于机器人部署的人工智能操作系统的创新可以对国家供应链的各个方面产生积极影响，并确保美国境内基本物品和消费品的快速和强大的分销网络。这个小企业创新研究（SBIR）第一阶段项目推进了机器人采摘的模拟到现实的迁移学习。通过生成数以百万计的模拟机器人抓取和传感器读数，深度神经网络可以训练为特定应用可靠地挑选和放置各种各样的物体。该项目将开发和评估机器人拣件的新算法，以开发灵活的机器人控制软件，用于各种物理实例的材料处理。研究目标是减少抓取策略的计算时间，同时规划跨多个工具的抓取，并将抓取策略与电子商务配送中心遇到的订单处理流程相结合。研究目标将在一组测试对象上的标准化机器人拾取系统上进行系统测试，以评估性能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to advance the development of a reliable, flexible, and scalable logistics network for distributing essential items and supplies. Recent projections suggest that by 2022, total US e-commerce sales will exceed $900 B. The market for piece-handling automation across US e-commerce is estimated at $9.6B.The process for getting items from producer to consumer involves many touchpoints where operators pick and pack individual items. These processes are currently manual and highly repetitive, incurring a high rate of injuries. Errors in these processes are costly to e-commerce providers and may result in critical supplies getting lost or significantly delayed. However, automating pick-and-pack has been challenging due to significant diversity in warehouse processes and requires new Artificial Intelligence (AI) robotic control systems that can manipulate a large number of unique items in warehouses. Innovations in AI operating systems for robotic deployments can positively impact all aspects of the national supply chain and ensure a rapid and robust distribution network of essential items and consumer goods within the United States.This Small Business Innovation Research (SBIR) Phase I project advance the translation of simulation-to-reality transfer learning for robotic picking. By generating millions of simulated robotic grasps and sensor readings, deep neural networks can be trained to reliably pick and place a wide variety of objects for a particular application. This project will develop and evaluate new algorithms for robotic piece picking to develop flexible robotic control software for material handling across a variety of physical instantiations. The research objectives are to decrease computation time for grasping policies, plan grasps across multiple tools simultaneously, and integrate grasp policies with order handling processes encountered in e-commerce distribution centers. The research objectives will be systematically tested on a standardized robotic picking system on a set of test objects to evaluate performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.