Towards Industry 5.0: Emerging trends in dual-arm human-robot collaboration

Abstract

As industry transitions from the Industry 4.0 paradigm to the human-centered vision of Industry 5.0, robots are evolving from mere automation tools into intelligent partners capable of close collaboration with humans. Within this context, dual-arm human–robot collaboration (DA-HRC) has emerged as a key technology due to its human-like dexterity and ability to perform complex tasks. However, integrating dual-arm systems into collaborative environments is far more complex than merely doubling the number of manipulators; it also introduces a leap in system complexity, including challenges such as kinematic decoupling, dynamic coupling, and safe coordination. Despite growing research efforts, a comprehensive review of this inherently interdisciplinary field is still absent. This paper systematically surveys the literature on DA-HRC, with a particular focus on three core aspects: control, learning, and perception. It further discusses practical applications in industrial manufacturing and domestic service scenarios. The review highlights the ongoing paradigm shift from model-driven to data-driven approaches and identifies the integration of verifiable safety guarantees from traditional control theory with the adaptability of modern learning methods as a central technical challenge. Achieving safer, more efficient, and more natural interactions in dual-arm robots requires continuous iteration and deeper integration of these approaches.

Keywords

Dual-arm robots / Human–robot collaboration / Compliant control / Robot learning / Foundation models

CRediT authorship contribution statement

Jinhua Ye: Writing – review & editing, Writing – original draft, Methodology, Conceptualization. Yechen Fan: Writing – review & editing, Methodology, Formal analysis, Data curation. Gengfeng Zheng: Validation, Supervision, Methodology, Conceptualization. Houde Dai: Resources, Project administration, Investigation. Chenyang Song: Software, Formal analysis, Data curation. Haibin Wu: Supervision, Funding acquisition. Yu Zhang: Project administration, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported in part by the Projects of Fujian University Industry-Academic Cooperation (2022H6016); in part by the Fujian Provincial Major Science and Technology Project (2024HZ026020); in part by the Project of the Ministry of Industry and Information Technology (2024-RL-FZSX-01); in part by the Fujian Young Scientific and Technological Personnel Training (2025350124); and in part by the Projects of Strategic Emerging Industries and Future Industries of Fuzhou-Xiamen-Quanzhou National Independent Innovation Demonstration Zone .

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