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Assigning repetitive and physically-demanding construction tasks to robotscan alleviate human workers's exposure to occupational injuries. Transferringnecessary dexterous and adaptive artisanal construction craft skills fromworkers to robots is crucial for the successful delegation of constructiontasks and achieving high-quality robot-constructed work. Predefined motionplanning scripts tend to generate rigid and collision-prone robotic behaviorsin unstructured construction site environments. In contrast, Imitation Learning(IL) offers a more robust and flexible skill transfer scheme. However, themajority of IL algorithms rely on human workers to repeatedly demonstrate taskperformance at full scale, which can be counterproductive and infeasible in thecase of construction work. To address this concern, this paper proposes animmersive, cloud robotics-based virtual demonstration framework that serves twoprimary purposes. First, it digitalizes the demonstration process, eliminatingthe need for repetitive physical manipulation of heavy construction objects.Second, it employs a federated collection of reusable demonstrations that aretransferable for similar tasks in the future and can thus reduce therequirement for repetitive illustration of tasks by human agents. Additionally,to enhance the trustworthiness, explainability, and ethical soundness of therobot training, this framework utilizes a Hierarchical Imitation Learning (HIL)model to decompose human manipulation skills into sequential and reactivesub-skills. These two layers of skills are represented by deep generativemodels, enabling adaptive control of robot actions. By delegating the physicalstrains of construction work to human-trained robots, this framework promotesthe inclusion of workers with diverse physical capabilities and educationalbackgrounds within the construction industry.

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