Construction work is currently challenged by high injury rates, stagnant productivity, labor shortages, and use of outdated workflows.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