The Open Group Limited Public Funding

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.

AI4Work will investigate practical methods and tools for optimal sharing of work between humans and AI/robots. AI and robotics are likely to be most powerful means for radical improvement of working conditions in diverse domains, as they can support human operators in diverse tasks starting from difficult and tedious manual labor tasks up to complex decision-making tasks. The vision of the AI4Work project is to improve communication and collaboration between humans, AI and robots, allowing for an improvement of the working conditions within different processes in organisations in several domains in terms of increased efficiency of work, reduction in stress upon employees, increased confidence in decision-making process etc. Due to the high level of uncertainty in modern organisations an appropriate balance between human and machine activities must be found. The key assumption is that to cope with the required flexibility and dynamics, Sliding Work Sharing (SWS), where this balance varies during the operation depending on the situational context, machine based confidence levels and human interactions, is likely to be the most appropriate for modern organisations. The key challenge of the project is to develop a set of common methods and tools (methodology framework, digital twin service platform, SW building blocks for SWS) that can be applied in diverse sectors and with different AI/robotics services, allowing for an effective experience exchange. The project will make use of living digital twins of working systems as a mean to increase efficiency and trustworthiness of AI/robotics solutions. By this, the project, aiming at improved quality of jobs and creating more decent work for human operators, will contribute to the acceptance of the AI/robots support of work in diverse domains. The project will be driven by six pilots in different sectors: logistics, manufacturing industry, construction, healthcare, education and agriculture.

INSAFEDARE aims to provide a toolkit to enable cost-effective and high assurance decision-making in the context of the processes that all stakeholders may follow as part of regulation of medical devices. The toolkit consists of scientific guidance on assurance, tools to retrieve datasets and assist application validation and a publicly available guidance work-group for sustainable support of the discipline. INSAFEDARE will investigate use, challenges, and opportunities real world and synthetic data-driven validation of devices. The project will provide guidance on quality and safety assurance of datasets as a tool for validation, and devices that have validated using data driven approaches. INSAFEDARE will investigate use of synthetic datasets and will identify how they can be used to establish assurance before the formal, established certification process, reducing the risk for developers and waste for regulatory bodies. Furthermore, the project will publish the findings as a public guidance, laying the ground for a standard development working group; and develop training and syllabus that can enhance the skills of stakeholders. Finally, the project will develop a tool for the discovery, integration, and query of multiple datasets, and a tool for supporting the sustainable, dynamic, and through-life surveillance of devices, capturing the impact of new evidence offered by newly published datasets.

Manufacturing, construction, and agriculture are major driving forces for the European economy and prosperity. Maintaining its competitiveness in these sectors demands highly efficient and flexible processes, and this can be achieved through digitization. Novel intelligent robotic capabilities that can be deployed side-by-side with humans and can operate and adapt to dynamic environments can accelerate this process. However, existing robotic systems cannot fit well into such settings as they are not versatile and flexiblen enough to automate certain tasks, cannot collaborate safely with humans in open and dynamic environments, nor are they easily and economically adaptable to process changes. The SOPRANO project coalesces multidisciplinary research and innovation in human-robot collaboration and intelligent multi-agent systems, aspiring to design the next generation of manufacturing floors, construction sites, and agri-food production, where humans and intelligent machines will seamlessly work together. It proposes to scale collaboration from the single human-agent dyad to a peer-based synergy between multiple interconnected robotic systems featuring different physical and cognitive properties, supporting various tasks in collaboration with human workers, robotics and other agents. SOPRANO will validate the technological offering in three novel and open-access use cases addressing both large-scale industries and small to medium enterprises, adding value to EU key sectors and instrumenting community building surrounding the open-source technologies in the EU industrial ecosystem. During the project, we will also enable external SMEs and start-ups to benefit from the project technologies via an open call, which will enable the building of demonstrators using SOPRANO technologies that will open new market opportunities for their products and services.

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