A Survey of Training Healthcare Robots With Extended Reality and Digital Twins

Healthcare robots are cyberphysical systems designed to assist older adults and reduce the burden on healthcare professionals and family caregivers. These robots can perform various tasks, including delivering medications on time, promoting physical activity, and cultivating social connections by contacting family and friends. As the global population ages, healthcare robots are emerging as critical technology to support older adults and alleviate the burden on healthcare systems. However, their widespread adoption is hindered by significant challenges, including inflexible hard-coded functionalities, high development and training costs, and a common lack of cultural adaptability. Extended reality (XR) and digital twin (DT) technologies offer a transformative approach to overcome these hurdles by enabling safe, scalable, and cost-effective virtual training environments. This paper presents a systematic review of the literature at the intersection of XR, DT, and healthcare robotics, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We not only present a thematic analysis of current research but also identify key fundamental challenges in existing robot training methodologies and analyse the field's evolutionary trends through visualisations. Based on our synthesis, we propose a conceptual framework and guidelines for developing future healthcare robots that are not only technologically advanced but also personalised, empathetic, and culturally sensitive. The purpose of this survey is to provide a roadmap for researchers and practitioners to take advantage of these convergent technologies, with the intention of creating a more effective and humane healthcare ecosystem for ageing populations around the world.