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: 1928448
• 负责人: Aaron Dollar
• 金额: $ 152万
• 依托单位: Yale 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 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1928448&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.

这一人类-技术前沿的未来工作(FW-HTF)项目研究了一种新颖的人类-机器人协作架构，以提高回收行业的效率和盈利能力，同时重新创造更安全、更清洁和更有意义的回收工作。具体目标是改进废物分类过程，即将混合废物分离成塑料、纸张、金属、玻璃和不可回收物。美国废品回收行业--代表着每年1170亿美元的经济活动和超过53万个美国就业岗位--正努力满足国内和国际市场日益严峻的标准。该行业面临的一个主要问题是废物分类不善，导致材料杂质和回收产品的质量和价值显著下降。人类的感知和判断对于处理废流的对象种类、杂乱程度和变化的特性是必不可少的。然而，垃圾分类工人目前面临着健康风险和不适，这些风险和不适来自尖锐和沉重的物体、有毒材料、噪音、振动、灰尘、恶臭以及糟糕的供暖、通风和空调。该项目的创新机器人部分，特别是在物体检测、操纵和人-机器人交互方面，将允许新的分类设施架构，为人类工人创造新的、更安全的角色。该项目用经济分析来补充这些技术进步，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的端到端效率的设施配置。将调查人类和机器人之间的劳动分工，以提高工作满意度和工人的积极性，并纳入对工人福祉的考虑。特别是，该项目将探索如何利用机器人来放大工人的专业知识和价值。所有这些方面都将采取全面和相互关联的研究方法，包括开发机器人技术、设计人机界面、调查工人在新分类厂建筑中的角色，以及了解工人的需求和福祉并将其纳入设计过程。这项计划将开发适当的机器人技术，用于回收行业的部署，这将需要在废物分类和操作方面发展最先进的技术，以处理与回收设施相关的条件。基于深度神经网络的目标检测和语义分割框架将被设计用于丰富的多模式传感器数据，以解决关于高度杂乱、遮挡和对象多样性的挑战。基于动态和软操作策略的新型机器人操作算法将被用于从杂乱的废流中分离和挑选分类物品。将开发坚固而灵活的机器人硬件，包括机械臂和末端执行器。将设计和实施人机界面，以便在直观、高效和实用的工作流程中完成这些任务，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的来料分拣扩展到整个回收生态系统；现场材料加工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。相互作用的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格局。这些班次对员工满意度和积极性的影响将通过模拟系统与员工的面谈进行调查。新的技术版图将相应地形成，以改善工作体验。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

“We Make a Great Team!”: Adults with Low Prior Domain Knowledge Learn more from a Peer Robot than a Tutor Robot

“我们组成了一支伟大的团队！”：先前领域知识较低的成年人从同伴机器人那里学到的东西比从导师机器人那里学到的更多

• DOI: 10.1109/hri53351.2022.9889441
• 发表时间: 2022
• 期刊: ACM/IEEE International Conference on Human-Robot Interaction (HRI 2022
• 作者: Salomons, Nicole;Pineda, Kaitlynn Taylor;Adejare, Aderonke;Scassellati, Brian
• 通讯作者: Scassellati, Brian

A Social Robot for Anxiety Reduction via Deep Breathing

通过深呼吸减少焦虑的社交机器人

• DOI: 10.1109/ro-man53752.2022.9900638
• 发表时间: 2022
• 期刊: 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN
• 作者: Matheus, Kayla;Vazquez, Marynel;Scassellati, Brian
• 通讯作者: Scassellati, Brian

Robot Hand based on a Spherical Parallel Mechanism for Within-Hand Rotations about a Fixed Point

• DOI: 10.1109/iros51168.2021.9636704
• 发表时间: 2021-09
• 期刊: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Vatsal V. Patel;A. Dollar

An Analysis of Unified Manipulation with Robot Arms and Dexterous Hands via Optimization-based Motion Synthesis

基于优化的运动合成分析机械臂和灵巧手的统一操控

• DOI: 10.1109/icra48891.2023.10161325
• 发表时间: 2023
• 期刊: IEEE
• 作者: Patel, Vatsal V.;Rakita, Daniel;Dollar, Aaron M.
• 通讯作者: Dollar, Aaron M.

Open Robot Hardware: Progress, Benefits, Challenges, and Best Practices

• DOI: 10.1109/mra.2022.3225725
• 发表时间: 2022-12-19
• 期刊: IEEE ROBOTICS & AUTOMATION MAGAZINE
• 影响因子: 5.7
• 作者: Patel, Vatsal V.;Liarokapis, Minas V.;Dollar, Aaron M.
• 通讯作者: Dollar, Aaron M.