Robotics in construction: A critical review of the reinforcement learning and imitation learning paradigms

Davila Delgado, Manuel; Oyedele, Lukumon

Abstract

The reinforcement and imitation learning paradigms have the potential to revolutionise robotics. Many successful developments have been reported in literature; however, these approaches have not been explored widely in robotics for construction. The objective of this paper is to consolidate, structure, and summarise research knowledge at the intersection of robotics, reinforcement learning, and construction. A two-strand approach to literature review was employed. A bottom-up approach to analyse in detail a selected number of relevant publications, and a top-down approach in which a large number of papers were analysed to identify common relevant themes and research trends. This study found that research on robotics for construction has not increased significantly since the 1980s, in terms of number of publications. Also, robotics for construction lacks the development of dedicated systems, which limits their effectiveness. Moreover, unlike manufacturing, construction's unstructured and dynamic characteristics are a major challenge for reinforcement and imitation learning approaches. This paper provides a very useful starting point to understating research on robotics for construction by (i) identifying the strengths and limitations of the reinforcement and imitation learning approaches, and (ii) by contextualising the construction robotics problem; both of which will aid to kick-start research on the subject or boost existing research efforts.

• Journal Article Type: Article
• Acceptance Date: Oct 16, 2022
• Online Publication Date: Oct 29, 2022
• Publication Date: Oct 29, 2022
• Deposit Date: Oct 19, 2022
• Publicly Available Date: Oct 31, 2022
• Journal: Advanced Engineering Informatics
• Print ISSN: 1474-0346
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 54
• Article Number: 101787
• DOI: https://doi.org/10.1016/j.aei.2022.101787
• Keywords: Robotics; Construction; Reinforcement Learning; Imitation Learning; Deep Reinforcement Learning
• Public URL: https://uwe-repository.worktribe.com/output/10098484