Jul 2026· IEEE International Requirements Engineering Conference· pp. 273-285· 0 citations· 70 references
Computer Science
TL;DR
This work develops a two-level hierarchical taxonomy of Human-Robot Teamwork requirements derived from analysis of (academic and industrial) literature, standards and regulatory guidance, and examines how requirements distribute across Human-Led, RobotLed and Shared operational perspectives, revealing responsibility boundaries that shape safe collaboration.
Abstract
Autonomous systems are increasingly deployed in safety- and mission-critical domains where humans and robots must operate as a team to complete complex tasks. Existing requirements for Human-Robot teamwork remain fragmented across disparate sources, with no unified framework that addresses complexities of collaborative Human-Robot tasks. We address this gap by presenting a taxonomy of Human-Robot Teamwork (HRT) requirements derived from analysis of (academic and industrial) literature, standards and regulatory guidance. We extracted a construction corpus of 361 requirements from 14 cross-domain sources. Through iterative classification and refinement, we develop a two-level hierarchical taxonomy comprising 6 high-level categories and 21 low-level subcategories that distinguish information provision, relational control, decision support, safety mechanisms, performance monitoring, and foundational system capabilities. We validate the taxonomy through expert evaluation with 5 domain specialists and a utility demonstration on an independently assembled corpus of 448 requirements drawn from 19 sources spanning six HRT domains. The taxonomy classifies 412 of these requirements, with distributional patterns that converge with the construction corpus while revealing domain-specific patterns and gaps. We examine how requirements distribute across Human-Led, RobotLed and Shared operational perspectives, revealing responsibility boundaries that shape safe collaboration. This work provides a structured foundation for Requirements Engineering (RE) practitioners to systematically elicit and specify HRT capabilities.
The evolving field of Human–Robot Collaboration (HRC) in the Architecture, Engineering, Fabrication, and Construction (AEFC) academia and Industry requires new approaches that reconcile human cognitive adaptability with robotic precision to address the unpredictability of construction environments. Architectural fabric...
Tahmures Ghiyasi, Ali Ghazvinian· Architectural Intelligence· 0 citations
This work introduces a unified formalism for proactive robot assistance, organize it into three levels, and provides a framework to address the highest level of unprompted proactive assistance, and presents a method, GAP, that instantiates the framework, learning from passive observation to anticipate user goals and ac...
INTRODUCTION
Robotic systems are increasingly being introduced into construction to address persistent safety challenges, limited productivity growth, and skilled labor shortages. However, the collaboration between humans and robots introduces new interaction dynamics that can reshape human behavioral state and respons...
Nana Kwabena Asamoah Antwi, Gilles Albeaino, C. Nnaji· Journal of Safety Research· 0 citations
Human-robot collaboration (HRC) is established in robotics, but its value in laboratory automation remains underdemonstrated. This industrially informed critical review asks when retained scientist involvement is a defensible HRC opportunity, and when it instead signals current automation design limitations. The aim is...
Tye Cameron-Robson, Er-Fu Yang, Lynn Donlon et al.· International Conference on...· 0 citations
Achieving effective human–robot collaboration (HRC) in dynamic environments like construction requires robots to function as proactive peers, anticipating human intents and acting in advance. While current literature analyzes enabling technologies through a sense–plan–act lens or catalog construction applications, th...
Zao-Lin Pan, Yan-Tao Yu, Heng Li· Journal of construction engi...· 0 citations
It is suggested that the benefit of the agentic framework lies primarily in interaction quality rather than conversational efficiency, and the agentic architecture displayed robustness by recovering from non-normative inputs while maintaining strict goal alignment.
Morten Roed Frederiksen· 2 citations
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