ARISE will make industrial HRI deployments simpler, cheaper, and more widespread in Europe by developing and demonstrating the concept of AgileHRI. The ARISE project envisions a near future which aligns with the principles of Industry 5.0, prioritising resilient, sustainable, and human-centric work environments. In such a future, companies recognise that investing in industrial human-robot interaction (HRI) is essential for achieving better short- and long-term goals, rather than a cost. Human-centric approaches surpass traditional technology-driven approaches, with technology serving people rather than the other way around. Industrial HRI establishes its position as a game-changing asset that enables seamless collaboration between humans and robots on complex tasks, allowing them to work together in shifts of any length. On its way to materialise such a vision, the ARISE project will i) address major application challenges from today’s industry, ii) develop human-centric solutions, tools, and software modules which expand the state-of-the-art in industrial HRI, and iii) deploy industrial HRI at scale in four testing and experimentation facilities and more than 25 workplaces across Europe (FSTP Projects). The ARISE project will address these challenges using cutting-edge open-source technologies from the European innovation ecosystem and will make a significant adavance on their state-of-the-art to position Europe globally at the forefront of industrial HRI. To that aim, the project will put the focus on the achievement of four major goals: (1) to increase the efficiency and cost-effectiveness of developing, deploying, and maintaining HRI solutions; (2) To develop open-source based reusabmodules which push industrial HRI beyond the SotA; (3) to demonstrate openness and agility as crucial enablers of truly valuable and sustainable HRI solutions; (4) to ensure impact a and sustainability through a critical mass of stakeholders & strong liaisons with ADRA ecosystem.

The HumanE AI Net brings together top European research centers, universities and key industrial champions into a network of centers of excellence that goes beyond a narrow definition of AI and combines world leading AI competence with key players in related areas such as HCI, cognitive science, social sciences and complexity science. This is crucial to develop a truly Human Centric brand of European AI. We will leverage the synergies between the involved centers of excellence to develop the scientific foundations and technological breakthroughs needed to shape the AI revolution in a direction that is beneficial to humans both individually and societally, and adheres to European ethical values and social, cultural, legal, and political norms. The core challenge is the development of robust, trustworthy AI capable of what “understanding” humans, adapting to complex real-world environments, and appropriately interacting in complex social settings. The aim is to facilitate AI systems that enhance human capabilities and empower individuals and society as a whole while respecting human autonomy and self-determination. The HumanE AI Net project will engender the mobilization of a research landscape far beyond direct project funding, involve and engage European industry, reach out to relevant social stakeholders, and create a unique innovation ecosystem that provides a many fold return on investment for the European economy and society. We will make the results of the research available to the European AI community through the AI4EU platform and a Virtual Laboratory, develop a series of summer schools, tutorials and MOOCs to spread the knowledge, develop a dedicated innovation ecosystem for transforming research and innovation into an economic impact and value for society, establish an industrial Ph.D. program and involve key industrial players from sectors crucial to European economy in research agenda definition and results evaluation in relevant use cases.

Algebraic Machine Learning (AML) has recently been proposed as new learning paradigm that builds upon Abstract Algebra, Model Theory. Unlike other popular learning algorithms, AML is not a statistical method, but it produces generalizing models from semantic embeddings of data into discrete algebraic structures, with the following properties: P1: Is far less sensitive to the statistical characteristics of the training data and does not fit (or even use) parameters P2: Has the potential to seamlessly integrate unstructured and complex information contained in training data, with a formal representation of human knowledge and requirements; P3. Uses internal representations based on discrete sets and graphs, offering a good starting point for generating human understandable, descriptions of what, why and how has been learned P4. Can be implemented in a distributed way that avoids centralized, privacy-invasive collections of large data sets in favor of a collaboration of many local learners at the level of learned partial representations. The aim of the project is to leverage the above properties of AML for a new generation of Interactive, Human-Centric Machine Learning systems., that will: - Reduce bias and prevent discrimination by reducing dependence on statistical properties of training data (P1), integrating human knowledge with constraints (P2), and exploring the how and why of the learning process (P3) - Facilitate trust and reliability by respecting ‘hard’ human-defined constraints in the learning process (P2) and enhancing explainability of the learning process (P3) - Integrate complex ethical constraints into Human-AI systems by going beyond basic bias and discrimination prevention (P2) to interactively shaping the ethics related to the learning process between humans and the AI system (P3) - Facilitate a new distributed, incremental collaborative learning method by going beyond the dominant off-line and centralized data processing approach (P4)