Micro-ROS will be a Robotic framework targeting embedded and deep embedded robot components with extremely constrained computational resources. These devices have special characteristics: minimum real time operating system or no operating system, battery powered, wireless low bandwidth connections, and intermittent operation with sleep periods. Micro-ROS will be compatible with the Robot Operating System (ROS 2.0), the de facto standard for robot application development. Micro-ROS will enable the interoperability of traditional robots with IoT sensors and devices, creating truly distributed robotic systems using a common framework. Micro-ROS empowers these computational devices with constrained resources to become first class participants of the ROS ecosystem allowing to create smaller robots using the same tools as well as taking advantage of the increasing overlapping between robotics, smart embedded devices and IoT.

DIH^2 is a network of 26 DIHs, with a target to reach over 170 DIHs. The sole aim of the network is to spark incremental (cut 50% cost of advance robotics solutions, double the growth of robotics market) and disruptive (maximum productivity & optimum agility) innovations in over 300,000 Manufacturing SMEs and Mid-Caps. It will support SMEs in their Agile Production challenge (50% increase in productivity) and unleash their digitalization potential by enabling robot solutions that are more cost effective at lower lot sizes. DIH^2 relies on: • A Common Open Platform Reference Architecture for Agile Production (COPRA-AP) -based on Industrial Data Space Reference Architecture Model and FIWARE technologies- to serve the needs of SMEs by means of a continuously growing set of Robotic-based Open Standard Enablers (ROSE-AP). • A marketplace as one-stop-shop for SMEs to access essential services for digital transformation including business modelling, technical support, access to skills and finance. • 2 competitive Open Calls to launch an ambitious Technology Transfer Program with 260 Agility Audits, 26 cross-border Technology Transfer Experiments and 26 ROSE-AP, leveraging over 26M€ of public & private funding in advance robotics solutions for Agile Production. • Members deeply rooted in their regional Smart Specialization Strategy (bringing €5M additional funding on top of EU funds), ensuring ‘working distance’ services for every SME in Europe - whichever the sector, wherever the location, whatever the size. • A Corporate Sponsorship Program from equipment and automation suppliers committed with the network to get access to wider market and latest research in robotics. DIH^2 will transform this network into a self-sustainable non-profit association with members all over Europe. DIH^2 will demonstrate that public funded research can help SMEs & Mid-Caps achieve digital excellence and global competitiveness through adopting advanced robotics solutions in Agile Production.

AI is increasingly becoming a significant factor in the CO2 footprint of the European economy. To avoid a conflict between sustainability and economic competitiveness and to allow the European economy to leverage AI for its leadership in a climate friendly way, new technologies to reduce the energy requirements of all parts of AI system are needed. A key problem is the fact that tools (e.g. PyTorch) and methods that currently drive the rapid spread and democratization of AI prioritize performance and functionality while paying little attention to the CO2 footprint. As a consequence, we see rapid growth in AI applications, but not much so in AI applications that are optimized for low power and sustainability. To change that we aim to develop an interactive design framework and associated models, methods and tools that will foster energy efficiency throughout the whole life-cycle of ML applications: from the design and exploration phase that includes exploratory iterations of training, testing and optimizing different system versions through the final training of the production systems (which often involves huge amounts of data, computation and epochs) and (where appropriate) continuous online re-training during deployment for the inference process. The framework will optimize the ML solutions based on the application tasks, across levels from hardware to model architecture. AI developers from all experience levels will be able to make use of the framework through its emphasis on human-centric interactive transparent design and functional knowledge cores, instead of the common blackbox and fully automated optimization approaches in AutoML. The framework will be made available on the AI4EU platform and disseminated through close collaboration with initiatives such as the ICT 48 networks. It will also be directly exploited by the industrial partners representing various parts of the relevant value chain: from software framework, through hardware to AI services.

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)

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.