The success of 5G technologies depends closely on their ability to attract vertical stakeholders, seeking the move of their services from cloud to the edge to meet unique KPIs. 5G-INDUCE project is based on the belief that such attractiveness requires vertical stakeholders and Network Application (nApp) developers to be able to smoothly deploy and manage applications in distributed 5G network environments, in a secure fashion and with strict KPI requirements. 5G-INDUCE relies on the deployment of an open ETSI NFV compatible 5G orchestration platform for the deployment of advanced 5G nApps. The platform’s unique features provide the capability to the nApp developers to define and modify the application requirements while the underlay intelligent OSS can expose the network capabilities to the end users on the application level without revealing any infrastructure related information. This process enables an application-oriented network management and optimization approach that is in line with the operator’s role as manager of its own facilities, while it offers the operational environment to any developers and service providers through which tailored made applications can be designed and deployed, for the benefit of vertical industries and without any indirect dependency through a cloud provider. The project focuses on the Industry 4.0 vertical sector, as one of the fastest growing and most impactful sectors in European economy with high potentials for service development SMEs and with the capability to tackle all diverse cases of service requirements. The platform is integrated over 3 5G Experimentation Facilities in Spain, Greece, and Italy, and extended with links towards specific Industries, for the showcasing of nApps in real 5G environment. The consortium includes all the required stakeholders (MNOs, Industries, System integrators and SMEs) from the benefited business sectors evaluated in the project, while significant part of the work (>50%) is conducted by innovative SMEs.

As robots are entering unstructured environments with a large variety of tasks, they will need to quickly acquire new abilities to solve them. Humans do so very effectively through a variety of methods of knowledge transfer – demonstration, verbal explanation, writing, the Internet. In robotics, enabling the transfer of skills and software between robots, tasks, research groups, and application domains will be a game changer for scaling up the robot abilities. euROBIN therefore proposes a threefold strategy: First, leading experts from the European robotics and AI research community will tackle the questions of transferability in four main scientific areas: 1) boosting physical interaction capabilities, to increase safety and reliability, as well as energy efficiency 2) using machine learning to acquire new behaviors and knowledge about the environment and the robot and to adapt to novel situations 3) enabling robots to represent, exchange, query, and reason about abstract knowledge 4) ensuring a human-centric design paradigm, that takes the needs and expectations of humans into account, making AI-enabled robots accessible, usable and trustworthy. Second, the relevance of the scientific outcomes will be demonstrated in three application domains that promise to have substantial impact on industry, innovation, and civil society in Europe. 1) robotic manufacturing for a circular economy 2) personal robots for enhanced quality of life 3) outdoor robots for sustainable communities. Advances are made measurable by collaborative competitions. Finally, euROBIN will create a sustainable network of excellence to foster exchange and inclusion. Software, data and knowledge will be exchanged over the EuroCore repository, designed to become a central platform for robotics in Europe. The vision of euROBIN is a European ecosystem of robots that share their data and knowledge and exploit their diversity to jointly learn to perform the endless variety of tasks in human environments.

The MANiBOT research aims at bi-manual mobile robots, able to perform a wide variety of manipulation tasks with highly diverse objects, possibly partly or fully unknown beforehand, in a human-like manner and performance. To achieve this, we advance and fuse all necessary technologies, from multimodal perception, cognition and control, to novel cognitive mechatronics. We develop new environment understanding and object/pose recognition methods, empowered through adaptive, context-aware fusion of vision, proximity and tactile sensing and emphasizing on adequate efficiency for fast and effective manipulation, even of objects without precise model, including deformable ones, and in diverse, challenging environments with human presence. We also develop a novel suite of manipulation primitives including non-prehensile manipulations, which along with bi-manual manipulation will allow the transfer of diverse objects with various sizes, weights, shapes, materials and rigidities from a mobile robot, with performance close to that of humans, even upon significant spatial constraints. The above are fused through a novel approach for robot cognitive functions based on multi-level robot cycles that allow learning, composing and swiftly adapting robot behaviors for complex manipulations, covering key topics of sequential manipulation of multiple objects to achieve complex goals. We push the limits of physical intelligence of bimanual mobile robots by coupling our methods with novel cognitive mechatronics, fusing advanced tactile and proximity sensors with a bi-manual mobile manipulator, optimized for energy efficiency and increased autonomy, including HRI capabilities for trustworthy and efficient operation. Our outcomes will be evaluated in four use cases, in three pilot sites (TRL5), in challenging environments where the handling of abundance of different objects is needed; in retail/supermarkets and transport/airports, for shelves restocking and baggage handling operations.