XR5.0 Project
Project description
XR5.0 will build, demonstrate, and validate a novel Person-Centric and AI-based XR paradigm that will be tailored to the requirements and nature of I5.0 applications. In this direction, the project will specify structuring principles and blueprints for using XR in I5.0 applications with emphasis on the development of innovative “XR-made-in-Europe” technology that blends with human-centric manufacturing technologies and adheres to European values. The XR5.0 applications will consider the characteristics and context of the worker based on the integration of human-centred digital twins (DTs) that comprise the “digital image” of the worker. At the same time, XR5.0 will design and implement a unique blending of XR technology and advanced AI paradigms, including AI technologies that foster the interplay between humans and AI such as explainable AI (XAI), Active Learning (AL), Generative AI (GenAI), and neurosymbolic learning. The XR5.0 technologies will be coupled with a cloud-based XR training platform for Operator 5.0 applications, which will enable ergonomic and personalized training of industrial workers on popular processes. The XR5.0 paradigm will empower the development of six (6) novel high-TRL pilot applications spanning the areas of AI-based product design, remote and intelligent maintenance of assets, workers’ training, support in product assembly, as well as guidance and instructions for troubleshooting. These applications will be demonstrated in realistic manufacturing environments. Moreover, they will be integrated to the EU XR platform to be developed as part of the call. Most importantly, XR5.0 will build a vibrant community of interested stakeholders around the project’s outcomes. This community will provide a basis for the sustainability and wider uptake of the project’s results towards maximising the impact of the project’s use cases. In this direction, all XR5.0 technologies will be high TRL>=7-8 and ready for immediate commercialisation.
Project outputs
Publications
| Domain |
Type of output |
Title |
DOI URL
|
| AI, Machine Learning & Data Science |
Conference proceedings |
A Scalable Data-Driven Methodology for Human Intention Prediction in Diverse Collaborative Scenarios. |
https://doi.org/10.5281/ZENODO.15720280
|
| AI, Machine Learning & Data Science |
Conference proceedings |
A Protocol for Human-Centric Adaptive User Interfaces: From Static Interaction to Behaviour-Driven Adaptation. |
https://doi.org/10.5281/ZENODO.15720235
|
| AI, Machine Learning & Data Science |
Conference proceedings |
Bridging Industrial Expertise and XR with LLM-Powered Conversational Agents |
https://doi.org/10.48550/ARXIV.2504.05527
|
| AI, Machine Learning & Data Science |
Conference proceedings |
Integrating Asset Administration Shell with an IIoT Platform for Human-centric Digital Twins |
https://doi.org/10.5281/ZENODO.16283191
|
| AI, Machine Learning & Data Science |
Peer reviewed articles |
Bias in Machine Learning: A Literature Review |
https://doi.org/10.3390/APP14198860
|
| Ethics, Society, Arts & Culture |
Conference proceedings |
UI/UX Sustainable Design: Best Practices for Applications CO2 Emissions Reduction |
https://doi.org/10.23919/SPLITECH61897.2024.10612495
|
| Robotics, Manufacturing & Industry 4.0 |
Book chapters |
Improving Collaborative Robotics: Insights on the Impact of Human Intention Prediction |
https://doi.org/10.1007/978-3-031-81688-8_1
|
| Robotics, Manufacturing & Industry 4.0 |
Book chapters |
Impact of Collaborative Robots on Human Trust, Anxiety, and Workload: Experiment Findings |
https://doi.org/10.1007/978-3-031-65894-5_28STYLE
|
| Robotics, Manufacturing & Industry 4.0 |
Conference proceedings |
XR5.0: Human-Centric AI-Enabled Extended Reality Applications for Industry 5.0 |
https://doi.org/10.23919/FRUCT64283.2024.10749931
|
