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.

翻译：将重复性且体力要求高的建筑任务分配给机器人，可减轻人类工人遭受职业伤害的风险。将人类工人所掌握的必要灵巧且适应性强的工艺技能迁移至机器人，对于成功委派建筑任务并实现高质量机器人施工成果至关重要。预定义的运动规划脚本在非结构化施工现场环境中往往会产生僵硬且易碰撞的机器人行为。相比之下，模仿学习提供了一种更稳健、更灵活的技能迁移方案。然而，大多数模仿学习算法依赖人类工人反复以全尺寸演示任务执行，这在建筑施工场景中可能适得其反且不可行。为解决这一问题，本文提出了一种基于沉浸式云机器人的虚拟演示框架，其具备两大核心功能：首先，该框架将演示过程数字化，消除了重复操控重型建筑物体的需求；其次，它采用联邦式可复用演示收集机制，这些演示可迁移至未来类似任务，从而减少人类操作者对任务的重复演示要求。此外，为增强机器人训练的可信度、可解释性与伦理合理性，该框架利用分层模仿学习模型将人类操作技能分解为序列性子技能与反应性子技能。这两层技能通过深度生成模型表征，可实现机器人动作的自适应控制。通过将建筑作业的体力负担转移至经人类训练的机器人，该框架促进了不同体能能力与教育背景的劳动者在建筑行业中的包容性参与。