Principal Investigator: Burcin Becerik-Gerber
CoPrincipal Investigator(s): Lucio Soibelman, Yasemin Copur-Gencturk, Gale Lucas
Organization: University of Southern California
Abstract:
The construction industry is one of the largest industries in the United States, employing millions of workers, however it is challenged by low productivity rates, worker shortages, and safety concerns. The industry has a tremendous opportunity to improve its productivity and safety with the recent advancements in technology. With the increased level of automation on construction sites, workers need to learn how to work with these new technologies and gain new knowledge and engineering skills. The goal of this project is to create and test a training program delivered through virtual reality (cyberlearning) to educate and re-educate workers for new work experiences that require collaboration with robots on construction sites. The project seeks to improve the teamwork among workers and robots on construction sites, focusing on more than one specific skill set and providing a comprehensive learning experience. The project is significant because it is a pioneering effort in providing learning opportunities to workers with varying levels of language proficiency and education, preparing them for work at the human-technology frontier. Fundamental questions addressed by the project include: 1) How does cyberlearning increase workers’ knowledge, safety behavior and trust in automation compared to the traditional training methods? 2) How do individual differences impact workers’ cyberlearning? 3) How does trust in automation change based on the construction task? 4) How does cyberlearning affect productivity and safety on construction sites? 5) Does the knowledge and level of trust gained through cyberlearning carry over to actual construction sites? 6) How does cyberlearning influence the development of next generation of construction automation? Through the exploration of these research questions, this project provides evidence for the utility of cost-effective training programs for vocational workforce of the construction industry.
Numerous learning scenarios and types of construction robots are included in the cyberlearning platform, which is developed in Task 1. A construction site is simulated via the use of discrete event simulations and immersive virtual environments where construction workers interact with robots on a given task. To move away from today’s lab-based hands-on training, the cyberlearning environment incorporates dynamic construction work environments, such as multiple tasks and crews sharing the same space and conditions, for example, uneven terrain, dust, rain through simulations and by using accurate models of construction sites. The work advances knowledge on the impact of cyberlearning for worker-robot teamwork at two levels: user-level and site-level. For the user-level investigations in Task 2, the work explores: 1) the extent to which cyberlearning increases workers’ knowledge, safety behavior, and trust in automation, 2) how individual differences moderate workers’ learning and 3) how trust-in-automation changes based on task type. For the site-level investigations in Task 3, the work explores: 1) the extent to which cyberlearning impacts productivity, safety, and trust-in-automation compared to in-person training across all workers on construction sites, and 2) how to both improve the cyberlearning environment and the construction robots for deployment on construction sites. Finally, through simulation-based studies in Task 4, the work explores how human-robot interactions impact construction tasks and workflows, safety procedures and productivity. The cyberlearning program is designed to maximize the extent to which the workers accept and trust construction robots, while at the same time freeing up human resources, improving productivity and safety, both of which are important to both individual workers and society at large.
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.
