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

这个“人类技术前沿的未来工作”（FW-HTF）项目研究了一种新型的人机协作架构，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。具体目标是改善废物分类过程，即将混合废物分为塑料、纸张、金属、玻璃和不可回收物。美国废品回收行业每年的经济活动为1170亿美元，创造了53万多个就业岗位，目前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佳，导致材料杂质和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种类、杂乱程度和不断变化的特性至关重要。然而，垃圾分类工人目前面临着尖锐和沉重的物体、有毒材料、噪音、振动、灰尘、恶臭以及供暖、通风和空调不良所带来的健康风险和不适。该项目的创新机器人组件，特别是在物体检测，操作和人机交互方面，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来补充这些技术进步，以确定最能消除加工瓶颈的设施配置，目标是高价值材料，并提高回收过程的端到端效率。将研究人类和机器人之间的劳动分工，以提高工作意愿和工人的积极性，并考虑工人的福祉。特别是，该项目将探索如何利用机器人来扩大工人的专业知识和价值。在所有这些方面，将采取一种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，调查工人在新分拣厂架构中的角色，并了解工人的需求和福祉并将其纳入设计过程。该项目将开发适当的机器人技术用于回收行业部署，这将需要提高废物分类和处理的技术水平，以处理与回收设施有关的条件。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态传感器数据进行设计，以解决高水平的杂波、遮挡和对象多样性方面的挑战。基于动态和软操作策略的新型机器人操作算法将用于从杂乱的废物流中分离和挑选分类物品。将开发坚固和灵巧的机器人硬件，包括机器人手臂和末端执行器。人机界面将被设计和实施，以实现这些任务在一个直观，高效和实用的工作流程，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的来料分类扩展到整个回收生态系统;现场材料处理单元的额外工艺线将能够将部分成品输送到下一阶段制造商。这种扩张将需要一种新的系统方法，并将有助于实现更有效的回收工厂和更全面的就业阶梯，为现有和新的工人。这些技术和结构的变化，在工作的互动系统将改变双方的任务和关系的工作景观。这些变化对工人的满意度和动机的影响将通过模拟系统的工人访谈进行调查。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。

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.