CAREER: Co-Adaptation and Trust in Worker-Robot Interactions: Scalable Adoption of Collaborative Robots in Construction

职业：工人与机器人交互中的共同适应和信任：在建筑中大规模采用协作机器人

• 批准号: 2047138
• 负责人: Reza Akhavian
• 金额: $ 69.13万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047138&HistoricalAwards=false
• 关键词: CAREER; Co; Adaptation; Trust; Worker

Construction work is currently challenged by high injury rates, stagnant productivity, labor shortages, and use of outdated workflows. In the future, robotics may offer unprecedented opportunities to address these issues, but robotics has yet to fully address the range of technical challenges that arise from unstructured environments such as cluttered construction job sites and impoverished trust among workers who may not accept robots as a collaborative partners. This Faculty Early Career Development (CAREER) project will advance the NSF mission to promote the progress of science and to advance national health, prosperity, and welfare by advancing a fundamental understanding of adaptive technology for human activity and intent recognition and robot learning algorithms that can promote worker safety during construction materials handling scenarios. Human activity and intent recognition will be performed by directing real-time signals from wearable and environmental sensors to advanced machine learning and neural network algorithms. Worker safety during materials handling will be promoted using a robot motion planner that optimizes, in part, ergonomics of the material transfer from the robot to the human. Additionally, the project team will develop a model for trust-building and a framework for trust-calibration within worker-robot teams to ensure that construction workers accurately assess how much to trust their robotic partners on the job site. The project also includes an education and outreach component that builds STEM education capacity for a diverse group of individuals including high school students and their teachers, as well as undergraduate and graduate students.This use-inspired CAREER project contributes to a future in which collaborative robots (co-robots) learn from and assist construction workers, thereby decreasing physical workload while promoting ergonomic safety. Current robotics algorithms and applications fail to adapt to the unstructured complexity of construction job sites, and do not fully address the technical and behavioral challenges of the work, workers, and workplaces in the industry. This project focuses on construction material handling, a common and strenuous activity that can be facilitated using co-robots. This project will: (1) create safe robot-assisted material handling workflows through a co-adaptive robot learning system that responds to worker kinematics and muscle activities collected by wearable sensors; and (2) develop a model for trust-building and a framework for trust-calibration that aims to promote adoption of worker-robot teaming in construction. The project promises to advance fundamental knowledge in adaptive and reusable construction worker activity and intent recognition and will generate novel datasets and models. It will promote safety in robot-assisted materials handling tasks using a motion planner that optimizes ergonomic safety. Intelligent worker-robot teaming will be fostered by models of trust-building and trust calibration that can be used to guide worker-robot co-adaptation.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.

建筑工作目前面临着高伤害率、生产率停滞不前、劳动力短缺和使用过时工作流程的挑战。未来，机器人技术可能会为解决这些问题提供前所未有的机会，但机器人技术尚未完全解决非结构化环境带来的一系列技术挑战，例如杂乱无章的建筑工地，以及工人之间缺乏信任，他们可能不会接受机器人作为合作伙伴。该学院早期职业发展(CALEAR)项目将推进NSF的使命，通过促进对人类活动和意图识别的自适应技术以及机器人学习算法的基本理解，促进科学进步，促进国家健康、繁荣和福利，这些自适应技术可以在建筑材料处理场景中促进工人的安全。人类活动和意图识别将通过将来自可穿戴式和环境传感器的实时信号引导到先进的机器学习和神经网络算法来执行。使用机器人运动规划器，部分优化了从机器人到人类的材料传输的人体工程学，将提高工人在材料搬运过程中的安全。此外，项目团队将开发一个建立信任的模型和一个工人-机器人团队内部信任校准的框架，以确保建筑工人准确地评估在工作现场信任他们的机器人伙伴的程度。该项目还包括一个教育和推广部分，为包括高中生和他们的老师以及本科生和研究生在内的不同群体建立STEM教育能力。这个受使用启发的职业项目有助于创造一个协作机器人(协作机器人)向建筑工人学习和帮助的未来，从而在减少体力工作量的同时促进人体工程学安全。当前的机器人算法和应用无法适应建筑工地的非结构化复杂性，也不能完全解决行业中工作、工人和工作场所的技术和行为挑战。这个项目的重点是建筑材料处理，这是一种常见的、艰苦的活动，可以使用协作机器人来促进。该项目将：(1)通过共同适应的机器人学习系统，对可穿戴传感器收集的工人运动学和肌肉活动做出反应，创建安全的机器人辅助材料处理工作流程；(2)开发建立信任的模型和信任校准框架，旨在促进在建筑施工中采用工人-机器人合作。该项目承诺推进适应性和可重复使用的建筑工人活动和意图识别方面的基础知识，并将生成新的数据集和模型。它将使用优化人体工程学安全的运动规划器来促进机器人辅助材料处理任务的安全。智能工人-机器人合作将通过可用于指导工人-机器人共同适应的信任建立和信任校准模型来促进。该奖项反映了NSF的法定使命，并已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

Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning

• DOI: 10.48550/arxiv.2308.14843
• 发表时间: 2023-08
• 期刊: ArXiv
• 作者: Farid Shahnavaz;Riley Tavassoli;Reza Akhavian

Trust in Construction AI-Powered Collaborative Robots: A Qualitative Empirical Analysis

对人工智能驱动的建筑协作机器人的信任：定性实证分析

• 发表时间: 2023
• 期刊: 2023 ASCE International Conference on Computing in Civil Engineering
• 作者: Emaminejad, Newsha;Akhavian, Reza
• 通讯作者: Akhavian, Reza