Human–Robot Collaboration in Construction: Classification and Research Trends

TLDR

Construction robots are increasingly deployed on sites to enhance safety, efficiency, and productivity, and recent advances enable humans and robots to work side‑by‑side, creating a growing field of human‑robot collaboration with significant potential in construction. The paper proposes a new taxonomy for collaborative human‑robot work in construction teams and uses it to identify current challenges, knowledge gaps, and future research directions. The authors review two decades of construction robot evolution and define a five‑level taxonomy—Preprogramming, Adaptive Manipulation, Imitation Learning, Improvisatory Control, and Full Autonomy—based on robot autonomy and human effort.

Abstract

Construction robots continue to be increasingly deployed on construction sites to assist human workers in various tasks to improve safety, efficiency, and productivity. Due to the recent and ongoing growth in robot capabilities and functionalities, humans and robots are now able to work side-by-side and to share workspaces. The emerging field of human–robot collaboration has significant potential applications in construction and continues to advance the state of the art in defining the responsibilities of both humans and robots during collaborative work. This paper proposes a new taxonomy for collaborative human–robot work in construction teams. The evolution of construction robots during the last two decades is first reviewed, and relevant bodies of work are categorized into one of five levels of human–robot collaboration: Preprogramming, Adaptive Manipulation, Imitation Learning, Improvisatory Control, and Full Autonomy. The categories of the proposed taxonomy are defined based on the level of robot autonomy and the corresponding human effort in collaborative teamwork. Second, this paper uses the categories of the proposed taxonomy as a contextual framework to identify current challenges and knowledge gaps in collaborative human–robot construction work and recommends directions for future research.