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

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是推动一个可靠，灵活和可扩展的物流网络的发展，用于分发基本物品和用品。最近的预测表明，到2022年，美国电子商务销售总额将超过900 B美元。整个美国电子商务的件处理自动化市场估计为96亿美元。从生产商到消费者的过程涉及许多接触点，操作员挑选和包装单个物品。这些过程目前是手动的，重复性很高，造成的伤害率很高。这些流程中的错误对电子商务提供商来说代价高昂，可能导致关键供应丢失或严重延误。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，并确保在美国境内建立快速而强大的必需品和消费品分销网络。这个小企业创新研究（SBIR）第一阶段项目推进了机器人拣选的模拟到现实迁移学习的转化。 通过生成数百万个模拟机器人抓取和传感器读数，可以训练深度神经网络，以可靠地拾取和放置特定应用中的各种物体。该项目将开发和评估机器人工件拾取的新算法，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。研究的目标是减少计算时间的把握政策，计划把握多个工具同时进行，并集成把握政策与电子商务配送中心遇到的订单处理过程。研究目标将在标准化的机器人拣选系统上对一组测试对象进行系统测试，以评估性能。该奖项反映了NSF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.