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

建筑工作目前面临着高伤害率、生产力停滞、劳动力短缺和使用过时工作流程的挑战。在未来，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中出现的一系列技术挑战，例如杂乱的建筑工地和可能不接受机器人作为协作伙伴的工人之间的信任不足。 该教师早期职业发展（CAREER）项目将推进NSF的使命，以促进科学的进步，并通过推进对人类活动和意图识别的自适应技术的基本理解和机器人学习算法来促进国家的健康，繁荣和福利，这些算法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。 将使用机器人运动规划器来促进材料处理过程中的工人安全，该规划器在一定程度上优化了从机器人到人的材料转移的人体工程学。此外，该项目团队将开发一个信任建立模型和一个工人机器人团队内的信任校准框架，以确保建筑工人准确评估他们在工作现场对机器人合作伙伴的信任程度。该项目还包括教育和推广部分，旨在培养高中生及其教师以及本科生和研究生等多样化群体的STEM教育能力。这一以使用为灵感的CAREER项目有助于实现协作机器人（co-robots）向建筑工人学习并帮助他们的未来，从而减少体力劳动，同时提高人体工程学安全性。目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，也无法完全解决该行业工作、工人和工作场所的技术和行为挑战。该项目的重点是建筑材料处理，这是一项可以使用协作机器人来促进的常见而艰苦的活动。该项目将：（1）通过一个自适应机器人学习系统来创建安全的机器人辅助材料处理工作流程，该系统对可穿戴传感器收集的工人运动学和肌肉活动做出响应;（2）开发一个信任建立模型和一个信任校准框架，旨在促进在建筑中采用工人-机器人合作。该项目有望推进自适应和可重复使用的建筑工人活动和意图识别的基础知识，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性的运动规划器来促进机器人辅助材料处理任务的安全性。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Construction work is currently challenged by high injury rates, stagnant productivity, labor shortages, and use of outdated workflows.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.