People with physical impairments often face difficulties in performing daily tasks such as dressing, leading to dependence on caregivers. While robotic manipulators can provide valuable assistance, close physical interaction raises concerns over safety, comfort and trust, which can limit adoption. This article introduces the Assistive Robot Twin (ART) framework, a real-time digital twin that models both the user and the robot to enhance transparency during robot-assisted dressing. ART integrates two visual safety features: Bounding Boxes (BBs), which define static or dynamic protective zones around critical regions, and Trajectory Visualisation (TV), which displays planned robot movements in real time. We conducted a within-subject study with 36 participants mimicking stroke-related mobility impairment, evaluating six BB/TV configurations using validated interaction quality and system usability questionnaires. The results show that BBs significantly improved perceived safety ( \(\textrm{p} < 0.001\) ), reduced discomfort ( \(\textrm{p} < 0.001\) ) and increased trust ( \(\textrm{p} < 0.001\) ), with dynamic BBs providing the greatest safety benefits. TV significantly enhanced overall system usability ( \(\textrm{p}=0.007\) ), confidence and predictability of robot actions. While the study focuses on perceived interaction quality in a controlled setting with healthy participants, the results provide foundational evidence for the design of transparent assistive systems prior to clinical deployment.

Cyber–physical systems present a challenge to testers, bringing complexity and scale to safety-critical and collaborative environments. Digital twins enhance these systems through data-driven and simulation based models coupled to physical systems to provide visualisation, predict future states and communication. Due to the coupling between digital and physical worlds, digital twins provide a new perspective into cyber–physical system testing. The objectives of this study are to summarise the existing literature on digital-twin-based testing. We aim to uncover emerging areas of adoptions, the testing techniques used in these areas and identify future research areas. We conducted a systematic literature review which answered the following research questions: What cyber–physical systems are digital twins currently being used to test? How are test oracles defined for cyber–physical systems? What is the distribution of white-box, black-box and grey-box modelling techniques used for digital twins in the context of testing? How are test cases defined and how does this affect test inputs? We uncovered 26 relevant studies from 480 produced by searching with a curated search query. These studies showed an adoption of digital-twin-based testing following the introduction of digital twins in industry as well as the increasing accessibility of the technology. The oracles used in testing are the digital twin themselves and therefore rely on both system specification and data derivation. Cyber–physical systems are tested through passive testing techniques, as opposed to either active testing through test cases or predictive testing using digital twin prediction. This review uncovers the existing areas in which digital twins are used to test cyber–physical systems as well as outlining future research areas in the field. We outline how the infancy of digital twins has affected their wide variety of definitions, emerging specialised testing and modelling techniques as well as the current lack of predictive ability.

As robotic systems become more interactive and complex, there is a need to standardise interfaces and simplify development processes. This is particularly pertinent in the field of manufacturing, where human-robot collaboration is on the increase, but where standards and proprietary software are key barriers to deployment and adoption.In this article we present the ROSIE Adapter, a general-purpose, modular adapter developed in ROS designed to support the creation and connection of industry-ready digital twins. Together with our previous work on the modular CSI digital-twin framework, we demonstrate how the ROSIE Adapter creates a versatile "plug-and-play" interface that simplifies the development of new robotic processes, and improves accessibility to novice users. Furthermore, the adaptor supports integration of intuitive interface devices, such as speech and augmented reality interfaces, which enable more natural collaboration. We describe the adaptor and its use in two real-world applications, demonstrate the ease of use via a three-day hackathon event, and provide results showing the faithfulness of the arising digital twins to their connected physical systems.

We present a tool-supported approach to the synthesis, verification, and testing of the control software responsible for the safety of human-robot interaction in manufacturing processes that use collaborative robots. In human-robot collaboration, software-based safety controllers are used to improve operational safety, for example, by triggering shutdown mechanisms or emergency stops to reduce the likelihood of accidents. Complex robotic tasks and increasingly close human-robot interaction pose new challenges to controller developers and certification authorities. Key among these challenges is the need to assure the correctness of safety controllers under explicit (and preferably weak) assumptions. Our integrated synthesis, verification, and test approach is informed by the process, risk analysis, and relevant safety regulations for the target application. Controllers are selected from a design space of feasible controllers according to a set of optimality criteria, are formally verified against correctness criteria, and are translated into executable code and tested in a digital twin. The resulting controller can detect the occurrence of hazards, move the process into a safe state, and, under certain circumstances, return the process to an operational state from which it can resume its original task. We show the effectiveness of our software engineering approach through a case study involving the development of a safety controller for a manufacturing work cell equipped with a collaborative robot.