  Assigning repetitive and physically-demanding construction tasks to robots
can alleviate human workers's exposure to occupational injuries. Transferring
necessary dexterous and adaptive artisanal construction craft skills from
workers to robots is crucial for the successful delegation of construction
tasks and achieving high-quality robot-constructed work. 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 to repeatedly demonstrate task
performance at full scale, which can be counterproductive and infeasible in the
case of construction work. To address this concern, this paper proposes an
immersive, 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 thus reduce the
requirement for repetitive illustration of tasks by human agents. Additionally,
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
sub-skills. These two layers of skills are represented by deep generative
models, enabling adaptive control of robot actions. 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.
