Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots.

Assigning repetitive and physically demanding construction tasks to robots can alleviate human workers' exposure to occupational injuries, which often result in significant downtime or premature retirement. However, the successful delegation of construction tasks and the achievement of high-quality robot-constructed work requires transferring necessary dexterous and adaptive construction craft skills from workers to robots. Predefined motion planning scripts tend to generate rigid and collision-prone robotic behaviors in unstructured construction site environments. In contrast, imitation learning (IL) offers a more robust and flexible skill transfer scheme. However, the majority of IL algorithms rely on human workers repeatedly demonstrating task performance at full scale, which can be counterproductive and infeasible in the case of construction work. To address this concern, in this paper, we propose an immersive and Cloud Robotics-based virtual demonstration framework that serves two primary purposes. First, it digitalizes the demonstration process, eliminating the need for repetitive physical manipulation of heavy construction objects. Second, it employs a federated collection of reusable demonstrations that are transferable for similar tasks in the future and can, consequently, reduce the requirement for repetitive illustration of tasks by human agents. In addition, to enhance the trustworthiness, explainability, and ethical soundness of the robot training, this framework utilizes a hierarchical imitation learning (HIL) model to decompose human manipulation skills into sequential and reactive subskills. These two layers of skills are represented by deep generative models; these models enable adaptive control of robot action. The proposed framework has the potential to mitigate technical adoption barriers and facilitate the practical deployment of full-scale construction robots to perform a variety of tasks with human supervision. By delegating the physical strains of construction work to human-trained robots, this framework promotes the inclusion of workers with diverse physical capabilities and educational backgrounds within the construction industry. [ABSTRACT FROM AUTHOR]