Developed within the Horizon Europe programme, MASTER brings together expertise in XR, robotics, artificial intelligence and education to create a comprehensive ecosystem for teaching robotics in manufacturing environments.
The project's centre piece is the MASTER XR Platform, a scalable environment that enables the creation, deployment and management of immersive learning experiences for students, engineers, trainers and manufacturing professionals.
The MASTER XR Platform is built on the enterprise-grade XR infrastructure platform VIROO. The platform allows users to interact with robotic systems, industrial processes and manufacturing environments in virtual, augmented and mixed reality. By combining immersive technologies with robotics-focused functionalities, the platform enables learners to gain practical experience in safe and controlled environments, reducing the need for access to physical equipment while supporting a learning-by-doing approach.
Throughout the project, MASTER developed and validated innovative technologies designed to make robotics training more accessible, effective and intuitive. These include immersive robot programming tools, multimodal interaction methods combining gaze, voice and traditional inputs, ergonomics assessment solutions, safety training modules, robot simulation capabilities and AI-supported learning approaches.
The project also developed a series of XR training scenarios covering key topics in robotics and manufacturing, including safety and hazard awareness, ergonomics assessment and workplace optimisation, machine tending and pick-and-place programming, process automation, robot programming and human-robot collaboration. These scenarios were designed to provide realistic and engaging learning experiences while allowing users to acquire practical skills in risk-free environments.
A key aspect of the project was the validation of the platform and its training content through extensive user studies involving students, engineers and robot operators. More than 100 participants took part in validation activities conducted within the project and through demonstrations of technologies integrated via the first Open Call. The educational applications developed under the second Open Call are also expected to engage more than 500 additional participants, further expanding the validation of the MASTER ecosystem.
To ensure a robust evaluation process, MASTER implemented a structured assessment framework using recognised methodologies, such as the System Usability Scale (SUS) and the NASA Task Load Index (NASA-TLX). These evaluations measured usability, workload and overall user experience across a variety of training scenarios and technologies. The results demonstrated high levels of user satisfaction, with usability scores exceeding 80% across several pilot activities.
The validation activities showed that users reported high engagement levels when interacting with immersive training environments. The developed solutions also demonstrated strong usability and accessibility across different user groups. Studies also confirmed effective knowledge transfer, showing that XR-based learning approaches can support engaging and intuitive acquisition of robotics and manufacturing skills. Third-party technologies and educational applications were also incorporated into the MASTER ecosystem during the validation study, which confirmed the platform's openness and scalability.
The findings also indicate that XR-based approaches can reduce the time required to configure robotic applications while improving user understanding, interaction and confidence when working with robotic systems. This is particularly important in manufacturing environments, where the increasing adoption of automation requires workers to acquire new skills quickly and safely.
Among the technologies evaluated during the project were gaze-based interaction and eye-tracking functionalities, which form part of MASTER's multimodal interaction framework. The validation demonstrated strong usability of these interaction methods while providing valuable insights into user attention, behaviour and interaction patterns. These findings contribute to the development of more human-centred XR systems that can adapt to users' needs and support more effective learning experiences.
The validation activities also confirmed the successful integration of innovations developed through MASTER's Open Calls. The technologies contributed by the 17 projects funded under the first Open Call and the educational applications developed by the 24 projects selected under the second Open Call demonstrated how external innovators can successfully contribute to and expand the MASTER ecosystem. As the project approaches its end, the validation of the MASTER XR Platform, its training scenarios and its growing ecosystem shows that immersive technologies could support future robotics education and industrial training. By combining XR, robotics and AI within a flexible and scalable platform, MASTER has laid the foundations for more accessible, effective and engaging learning experiences that can help prepare Europe's workforce for increasingly automated and digitalised manufacturing environments.
About MASTER
As industries transition to Industry 4.0 production models, robots are becoming more prevalent in industrial processes. XR technologies are also entering this domain, with successful cases in training, remote assistance, and contextual information access.
MASTER enhances robotics training in manufacturing with its XR platform. It integrates key functionalities, creating safe and flexible robotic environments with advanced interaction mechanisms. The project delivers rich training content and invites third-party contributions through two Open Calls.
