FW-HTF-RL: Collaborative Research: Shared Autonomy for the Dull, Dirty, and Dangerous: Exploring Division of Labor for Humans and Robots to Transform the Recycling Sorting Industry

FW-HTF-RL：协作研究：沉闷、肮脏和危险的共享自治：探索人类和机器人的分工以改变回收分类行业

• 批准号: 1928477
• 负责人: Kate Saenko
• 金额: $ 37.5万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1928477&HistoricalAwards=false
• 关键词: FW; HTF; RL; Collaborative; Research

This Future of Work at the Human-Technology Frontier (FW-HTF) project investigates a novel human-robot collaboration architecture to improve efficiency and profitability in the recycling industry, while re-creating recycling jobs to be safer, cleaner, and more meaningful. The specific goal is to improve the waste sorting process, that is, the separation of mixed waste into plastics, paper, metal, glass, and non-recyclables. The US scrap recycling industry -- which represents $117 billion in annual economic activity and more than 530,000 US jobs -- is struggling to meet increasingly challenging standards in domestic and international markets. A major problem for the industry is poor sorting of waste, resulting in materials impurity and a significant decrease in the quality and value of the recycled product. Human perception and judgement are essential to handle the object variety, clutter level and changing characteristics of the waste stream. Yet waste-sorting workers currently face health risks and discomfort arising from sharp and heavy objects, toxic materials, noise, vibration, dust, noisome odors, and poor heating, ventilation, and air conditioning. The innovative robotics component of this project, especially in object detection, manipulation, and human-robot interaction, will allow new sorting facility architectures, creating new, safer roles for human workers. The project complements these technological advances with economic analyses to determine the facility configurations that best remove processing bottlenecks, target materials of high value, and boost the end-to-end efficiency of the recycling process. Division of labor between humans and robots will be investigated to improve job desirability and worker motivation, incorporating consideration of the workers' well-being. In particular, the project will explore ways to utilize robots to amplify worker expertise and value. A holistic and interconnected research approach will be taken for all these aspects, i.e. developing robotics technology, designing the human-machine interfaces, investigating workers' workers' role in the new sorting plant architectures, and understanding and incorporating workers' needs and well-being into the design process.This project will develop the appropriate robotics technology for recycling industry deployment, which will require advancing the state of the art in waste classification and manipulation to handle the conditions associated with recycling facilities. Deep Neural Networks-based object detection and semantic segmentation frameworks will be designed for rich, multi-modal sensor data in order to solve challenges regarding a high-level of clutter, occlusion and object variety. Novel robotic manipulation algorithms based on dynamic and soft manipulation strategies will be utilized to separate and pick classified items from the cluttered waste stream. Robust and dexterous robot hardware will be developed, including the robotic arms and end effectors. Human-machine interfaces will be designed and implemented to achieve these tasks in an intuitive, efficient and practical workflow that optimizes the contributions of both human workers and automated technologies. The robotics technology will also allow expanding the facilities from simply sorting the incoming materials into a whole recycling ecosystem; additional process lines for onsite materials processing units will enable conveying partially-finished products to next stage manufacturers. This expansion will require a novel systems approach, and will help achieve more efficient recycling plants and a much more comprehensive employment ladder for current and new workers. These technological and structural changes in the interactional system of work will shift both the task and relational landscape of the work. The effect of these shifts on worker satisfaction and motivation will be investigated via worker interviews with simulated systems. The new technological landscape will be formed accordingly for improved work experience.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.

人类技术前沿的工作未来 (FW-HTF) 项目研究了一种新颖的人机协作架构，以提高回收行业的效率和盈利能力，同时重新创造更安全、更清洁、更有意义的回收工作。具体目标是改进垃圾分类流程，即将混合垃圾分为塑料、纸张、金属、玻璃和不可回收物。美国废料回收行业每年的经济活动价值达 1,170 亿美元，为美国提供了超过 53 万个就业岗位，但该行业正在努力满足国内和国际市场日益严峻的标准。该行业的一个主要问题是废物分类不善，导致材料杂质以及回收产品的质量和价值显着下降。人类的感知和判断对于处理废物流的物体多样性、杂乱程度和不断变化的特征至关重要。然而，垃圾分类工人目前面临着由尖锐和重物、有毒材料、噪音、振动、灰尘、恶臭以及供暖、通风和空调不良引起的健康风险和不适。该项目的创新机器人技术，特别是在物体检测、操纵和人机交互方面，将允许新的分拣设施架构，为人类工人创造新的、更安全的角色。该项目通过经济分析补充了这些技术进步，以确定最能消除加工瓶颈、瞄准高价值材料并提高回收过程端到端效率的设施配置。将研究人类和机器人之间的分工，以提高工作满意度和工人积极性，并考虑工人的福祉。特别是，该项目将探索利用机器人来增强工人专业知识和价值的方法。所有这些方面都将采取整体和相互关联的研究方法，即开发机器人技术、设计人机界面、调查工人在新分拣工厂架构中的角色，以及理解和纳入工人的需求和福祉该项目将开发适合回收行业部署的机器人技术，这将需要推进废物分类和处理方面的最先进技术，以处理与回收设施相关的条件。基于深度神经网络的对象检测和语义分割框架将针对丰富的多模式传感器数据进行设计，以解决有关高级混乱、遮挡和对象多样性的挑战。基于动态和软操纵策略的新型机器人操纵算法将用于从杂乱的废物流中分离和挑选分类物品。将开发坚固且灵巧的机器人硬件，包括机械臂和末端执行器。将设计和实施人机界面，以直观、高效和实用的工作流程来完成这些任务，从而优化人类工人和自动化技术的贡献。机器人技术还将允许将设施从简单地对进料进行分类扩展到整个回收生态系统；现场材料加工装置的额外生产线将能够将半成品输送到下一阶段的制造商。这种扩张将需要一种新颖的系统方法，并将有助于实现更高效的回收工厂以及为现有和新工人提供更全面的就业阶梯。交互工作系统中的这些技术和结构变化将改变工作的任务和关系景观。这些转变对员工满意度和积极性的影响将通过模拟系统的员工访谈进行调查。新的技术格局将相应形成，以改善工作体验。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。