Artificial intelligence (AI) is adopted at a fast pace and is being applied widely in many domains. The aim of the REXASI-PRO project is to design a novel framework in which safety, security, ethics, and explainability are entangled to develop a Trustworthy Artificial Swarm Intelligence solution. The framework will make a trustworthy collaboration among a swarm formed by autonomous wheelchairs and flying robots to allow a seamless door-to-door experience for people with reduced mobility. Within REXASI-PRO-Hop-On, the widening partner (SIE) will address the following challenges, which were already identified in the REXASI-PRO proposal: - Foundational challenge 3: How to develop green AI systems without affecting the system’s performance? - Domain-specific challenge 4: How to preserve machine learning accuracy while reducing the power consumption of embedded computers? These two challenges are currently being addressed in the REXASI-PRO project by employing optimization and efficiency computational topology methods for energy saving, notably by limiting the amount of training data while maintaining the highest standards of completeness and accuracy. The REXASI-PRO-Hop-On proposal will address a different dimension of a green AI solution: algorithms for AI model compression, with the goal of optimizing the energy efficiency during AI model inference on the wheelchair;

The retail world is going through a period of strong evolution, accelerated by the ongoing COVID-19 pandemic. E-commerce has been shown to be a pillar of society and through necessity it outperformed physical shop retail in many product sectors, e.g. electronics, books. To cope with new social distancing measures being enforced by nearly all countries, in the near future traditional bricks-and-mortar retailers must introduce innovations that can help them to maintain their profit margins and support customers’ choice, access and most importantly their employees’ safety. Shoppers are demanding reduced waiting times, the possibility of buying a greater variety of products when they want them, i.e. 24-hour opening hours, and to feel safe when shopping. The solution we have developed in MiMEX, using intelligent shelves, and connected smart spaces targets these problems and opportunities. We propose to automate social distancing checks and adhere to regional safety protocols (such as wearing gloves and masks). We will exploit deep-learning algorithms and low-cost sensors to answer product choice and availability needs. We will create a complete hardware & software micro-market solution that can monitor shoppers’ safety and simultaneously track products/goods collected by customers in real-time inside a store, facilitating smart and safe checkout with bespoke payment systems. A complete monitoring system will make it possible to offer high-value services such as exhausted product alarms, attempted thefts or display-shelf tidying requests to retailers.

The revolutionary opportunities opened by eXtended Reality (XR) technologies will only materialize if concepts, techniques, and tools are provisioned to ensure the social acceptance of XR systems. For that, we need XR systems that are not just innovative and functionally complex, but also provide an experience that: satisfies the goals and needs of the user, is in compliance with the social context in which the system is being used, and is transparent, safe, secure, explainable and is trusted by the user. However, current generations of XR systems fail to provide the XR experience they were envisioned for since state-of-the-art models and technologies of XR systems fail to ensure full-fledged social acceptance. A truly XR experience requires a major paradigm shift in the way XR systems are designed, implemented, deployed and consumed. The SERMAS project will develop innovative, formal and systematic methodologies and technologies to model, develop, analyze, test and user-study socially-acceptable XR systems. This will be achieved by pursuing the following four main objectives: 1. Follow an inter-disciplinary, multi-sectorial, case-study-driven, scientific and technological methodology to implement the SERMAS Toolkit, a set of methods and tools that will greatly simplify the design, development, deployment, and management of socially-acceptable XR systems. 2. Apply the Toolkit to industrial case studies drawn from real-world application scenarios, thus paving the way to transferring project results to industrial practice. This will be possible through the active participation in the consortium of the developers of mass-use industrial XR applications. 3. Enable innovators to leverage the Toolkit to improve the social acceptance and cut down the time-to-market of their XR systems, thereby enhancing the competitiveness of the vendors. 4. Produce the wider SERMAS Methodology to position the use of the Toolkit and enlarge its outreach.

Agile Production crucially depends on the effectivity of intralogistics processes. Robots as components of these processes have the potential to be a game changer provided they are highly flexible, capable, cost- and energy-efficient, safe and able to operate in work environments shared with humans. However, the current state of the art falls short of providing these capabilities given the requirements for future production systems. Thus, DARKO sets out to realize a new generation of agile production robots that have energy-efficient elastic actuators to execute highly dynamic motions; are able to operate safely within unknown, changing environments; are easy (cost-efficient) to deploy; have predictive planning capabilities to decide for most efficient actions while limiting associated risks; and are aware of humans and their intentions to smoothly and intuitively interact with them. To maximise its impact, DARKO is aligned with use cases at the largest manufacturer of home appliances in Europe. It will demonstrate, in relevant scenarios, autonomous capabilities significantly beyond the current state of the art in dynamic manipulation (e.g., throwing of goods, picking and placing objects while in motion), perception, mapping, risk management, motion planning and human-robot interaction. Beyond its impact through improved capabilities in these areas, DARKO will provide answers to the questions where and how dynamic manipulation should be integrated as the most efficient solution in intralogistics. Since arm manipulators can, in principle, display super-human performance in terms of accuracy and repeatability, the value of integrating dynamic manipulation, e.g. throwing, into transport processes may well exceed current expectations. The DARKO consortium is uniquely placed to tackle this ambitious and challenging project. It brings together leading academic and corporate researchers, technology providers and end-users, with the required long-standing expertise.

As an interdisciplinary endeavour, the GREET network brings together leading institutions and companies in Cyber-Physical System (CPS), AI, software engineering and cognitive interaction research across Europe with the aim of training a new generation of scientists, technologists, and entrepreneurs that will move Europe into a leading role in the scientific and technological innovation in Generative AI-driven CPS, its proactivity and explainability. Linking the leading academic and industrial partners in the above areas, GREET will form an effective and unique training network powerfully combining the best research training with a range of academic and industrial placements, specialist research and knowledge transfer workshops. It will develop and train a new generation of young researchers through a set of key research projects, developing breakthrough CPS systems and services that feature generative cognition, explainee-aware generative explainability, and transparent proactivity in highly secured CPS eco-environments. It will shape the delivery of training in the novel generative AI-driven CPS across Europe: delivering key insights into the science and models of the proposed CPS, setting up its scientific foundation, and equipping the DN’s recruited Doctoral Candidates with skills to drive the next innovative steps in this key area of generative AI-driven CPS. These steps include building a sustainable innovation ecosphere and community, disseminating and exploiting this new CPS’s learning and understanding of the eco-internals, eco-surroundings and eco-dynamics in many vital societal and economic services, including Industry 5.0, smart city, healthcare, energy, emergency handling, social robotics and elderly care. The innovation perspective of these new CPS is substantial, which will be particularly important for handling services that involve humans and in critical situations, e.g., in emergency and hazardous environments.