Sommario
Training in robotics and industrial contexts is currently a complex challenge for universities, training providers and vocational education centres.
Access to advanced technologies such as industrial robotic arms is often limited by high costs, dedicated spaces and strict safety requirements. At the same time, the labour market increasingly demands advanced skills in human–robot collaboration, which are essential for the future of manufacturing and industrial sectors.
To address this need, TI-RAX – Teach Industrial Robotics with AI and XR was launched, a new project involving R2M Solution that leverages Artificial Intelligence (AI) and Virtual Reality (XR) to make industrial robotics training more accessible, safe and effective. The goal is to overcome economic and technological barriers by offering immersive learning experiences that prepare students and trainees for human–robot collaboration without the need for complex physical infrastructures.
TI-RAX introduces an innovative approach that combines immersive XR scenarios with AI-based educational environments, enabling trainers to design, test and refine training content before its delivery in the classroom.
Immersive scenarios for human–robot collaboration
At the core of TI-RAX is the development of two Virtual Reality training scenarios focused on real industrial use cases: automotive battery handling and refrigerator disassembly. In both scenarios, learners operate in a virtual environment, collaborating with robotic arms and experiencing realistic Human–Robot Collaboration (HRC) dynamics.
The scenarios are designed to require no prior industrial expertise and to be easily adopted by university students and vocational trainees. The XR environment enables learning in complete safety, significantly reducing the risks and costs typically associated with training on real industrial equipment.
Multi-agent AI to enhance educational quality
What sets TI-RAX apart is the integration of a multi-agent AI environment that supports educators in the design of training content. Before reaching real learners, the materials are “stress-tested” by virtual agents that simulate trainee behaviour by asking questions, highlighting ambiguities and challenging the clarity of explanations.
This approach enables rapid iterative refinement, improving learning effectiveness and going beyond the mere introduction of new technologies into traditional teaching methods. The objective is not only to teach how to use a robot, but also when and why to use it.
An XR ecosystem for more inclusive training
TI-RAX is embedded within the MASTER ecosystem, leveraging the capabilities of the eXRecise tool to convert textual materials into interactive XR scenes and to collect data for evaluating the learning experience. The final scenarios will be distributed via the VIROO platform, where trainees can interact immersively with the virtual environment using XR headsets and controllers.
Through this approach, TI-RAX contributes to the democratisation of industrial robotics training, supporting educators and trainers without specific XR expertise in the creation of new immersive learning modules.
