The construction industry is seeking a robotic revolution to meet increasing demands for productivity, quality, and safety. Typically, construction robots are usually pre-programmed for a single task, such as painting. Their behavior is fixed when they leave the factory. However, it is difficult to pre-program all capabilities (referred to as workplace skills) that construction workers may require. Construction robots are expected to have the same ability of skill learning as human apprentices, allowing them to acquire a wide range of workplace skills from experienced workers and eventually complete relevant construction tasks autonomously. However, workplace skill learning of robots has rarely been investigated in the construction industry. This survey reviews state-of-the-art approaches to help robots learn skills from human demonstrations. To begin, the workplace skill is represented as ‘Know That’ and ‘Know How’ problems. ‘Know That’ is a high-level task planning ability aimed at understanding human activities from demonstrations. ‘Know How’ refers to the ability to learn specific actions for completing the construction task. Sematic methods and learn from demonstration (LfD) methods are reviewed to tackle these two problems. Finally, we discuss the open issues of past research, present future directions, and highlight the survey's knowledge contributions. We believe that this survey will provide a new perspective on robots in the construction industry and inspire more discussions about skill learning of construction robots. © 2022 Elsevier Ltd
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