A multidimensional taxonomy for human-robot interaction in construction

Rodrigues, Patrick B.; Singh, Rashmi; Oytun, Mert; Adami, Pooya; Woods, Peter J.; Becerik-Gerber, Burçin; Soibelman, Lúcio; Çopur-Genctürk, Yasemin; Lucas, Gale M.

doi:10.1016/j.autcon.2023.104845

Cited by 19 publications

(2 citation statements)

References 75 publications

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“…In other words, construction work is labor intensive, cooperative, and notably difficult to automate. And yet, with some important exceptions [28][29][30][31], construction automation efforts commonly assume an ideal, simplified, version of construction [32][33][34][35], leaving unnoticed important attributes of real-world construction tasks and environments one could only see on sites. By contrast, in this paper, we employ an ethnographic approach to understand those attributes, and accordingly identify a case for the design and development of a robot aimed at adaptively supporting-rather than reducing or entirely performing-workers' roles in construction.…”

Section: Introductionmentioning

confidence: 99%

Robot in the Loop: An Ethnography-Based Approach to Robotics and Reinforcement Learning to Support On-Site Construction Work

Wu,

Erdolu,

Wei

et al. 2024

Preprint

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Construction is one of the world’s largest and least automated industries, relying on the cooperation of multiple people with diverse skillsets in labor intensive, physical tasks. Since the 1980s, efforts to introduce robots into construction contexts have mostly focused on automating discrete tasks or monitoring site activities. In this paper, by contrast, we show how robots might be designed to adaptively support the cooperative work of construction teams. Following an indicative review of the state of the art in construction robotics, the paper shows how a detailed ethnographic study of construction workers shaped the design, development, and evaluation of a robot able to assist a team of carpentry workers by delivering tools and hardware during the installation of formwork panels. The resulting prototype is a building companion rover guided by state-of-the-art deep reinforcement learning (DRL) methods and an innovative social navigation stack. Through quantitative and qualitative evaluations in lab settings and on a construction site, we show how the rover can adaptively support carpentry workers taking their specific workflow into account. By documenting these technical and conceptual contributions, we hope to bring ”robotically-supported construction” into focus as a domain of interest for the construction robotics community.

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Section: Introductionmentioning

confidence: 99%

Robot in the Loop: An Ethnography-Based Approach to Robotics and Reinforcement Learning to Support On-Site Construction Work

Wu,

Erdolu,

Wei

et al. 2024

Preprint

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“…Human-Robot Interaction (HRI): Dynamic interactions between humans and robots in collaborative settings, encompassing communication, cooperation, and the study of their collaboration in shared workspaces [20]. In construction, innovative categorizations have been developed leading to better approaches in Human-Robot Interaction problems [21].…”

mentioning

confidence: 99%

Enhancing Robot Behavior with EEG, Reinforcement Learning and Beyond: A Review of Techniques in Collaborative Robotics

Gonzalez-Santocildes,

Vazquez,

Eguiluz

2024

Preprint

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Collaborative robotics is a major topic in current robotics research posing new challenges, especially in Human-Robot Interaction. The crucial aspect in this area of research focuses on understanding the behavior of robots when engaging with humans, where reinforcement learning is a key discipline that allows us to explore sophisticated emerging reactions. This review aims to delve into the relevance of different sensors and techniques, with special attention to EEG (electroencephalography data on brain activity) and its influence on the behavior of robots interacting with humans. In addition, mechanisms available to mitigate potential risks during the experimentation process such as virtual reality will also be addressed. In the final part of the paper, future lines of research combining the areas of collaborative robotics, reinforcement learning, virtual reality and human factors will be explored, as this last aspect is vital to ensure safe and effective Human-Robot Interactions.

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Compound control method for reliability of the robotic arms with clearance joint

Fu,

Sun,

Song

et al. 2024

Meccanica

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A multidimensional taxonomy for human-robot interaction in construction

Cited by 19 publications

References 75 publications

Robot in the Loop: An Ethnography-Based Approach to Robotics and Reinforcement Learning to Support On-Site Construction Work

Robot in the Loop: An Ethnography-Based Approach to Robotics and Reinforcement Learning to Support On-Site Construction Work

Enhancing Robot Behavior with EEG, Reinforcement Learning and Beyond: A Review of Techniques in Collaborative Robotics

Compound control method for reliability of the robotic arms with clearance joint

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