RobMoSys will coordinate the whole community’s best and consorted effort to build an open and sustainable, agile and multi-domain European robotics software ecosystem. RobMoSys envisions an integration approach built on-top-of, or rather around, the current code-centric robotic platforms, by means of the systematic application of model-driven methods and tools that explicitly focus on (system-of-) system integration. As proven in many other engineering domains, model-driven approaches are the most suitable approach to manage integration that is intended to be “all-inclusive” with respect to technologies and stakeholder groups. RobMoSys will enable the management of the interfaces between different roles (robotics expert, domain expert, component supplier, system integrator, installation and deployment, operation) and separated concerns in an efficient and systematic way by making the step change to a set of fully model-driven methods and tools for engineering robotics systems. RobMoSys will drive the non-competitive part of building the eco-system aiming at turning community involvement into active support for an ecosystem of professional quality and scope. It will provide, based on broad involvement via two Open Calls, important concretizations for many of the common robot functionalities (sensing, planning, control in the broad sense). It will fulfill two complementary missions: (1) establish a common methodology enabling a composition-oriented approach to address complexity in robotics and face the integration burden caused by type diversity, target diversity and platform diversity; (2) stimulate and boost an ecosystem of methodology-based toolchains that supports the interaction of separated roles. RobMoSys is designed for widest inclusion - from the very beginning and throughout the overall course of the project - of the expertise and body of knowledge of the robotics community and of related relevant technology and application domains (Tier-1 concept).

The robots of tomorrow will be endowed with the ability to adapt to drastic and unpredicted changes in their environment including humans. Such adaptations can however not be boundless: the robot must stay trustworthy, i.e. the adaptations should not be just a recovery into a degraded functionality. Instead, it must be a true adaptation, meaning that the robot will change its behavior while maintaining or even increasing its expected performance, and stays at least as safe and robust as before. RoboSAPIENS will focus on autonomous robotic software adaptations and will lay the foundations for ensuring that such software adaptations are carried out in an intrinsically safe, trustworthy and efficient manner, thereby reconciling open-ended self-adaptation with safety by design. RoboSAPIENS will also transform these foundations into 'first time right'-design tools and robotic platforms, and will validate and demonstrate them up to TRL4. To achieve this over-all goal, RoboSAPIENS will extend the state of the art in four main objectives. 1. It will enable robotic open-ended self-adaptation in response to unprecedented system structural and environmental changes. 2. It will advance safety engineering techniques to assure robotic safety not only before, during and after adaptation. 3. It will advance deep learning techniques to actively reduce uncertainty in robotic self-adaptation. 4. It will assure trustworthiness of systems that use both deep-learning and computational architectures for robotic self-adaptation. To realise these objectives, RoboSAPIENS will extend techniques such as MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) and Deep Learning to set up generic adaptation procedures and also use an SSH dimension. RoboSAPIENS will demonstrate this trustworthy robotic self-adaptation on four industry-scale use cases centered around an industrial disassembly robot, a warehouse robotic swarm, a prolonged hull of an autonomous vessel, and human-robotic interaction.

As robots become more omnipresent in our society, we are facing the challenge of making them more socially competent. However, in order to safely and meaningfully cooperate with humans, robots must be able to interact in ways that humans find intuitive and understandable. Addressing this challenge, we propose a novel approach for understanding and modelling social behaviour and implementing social coupling in robots. Our approach presents a radical departure from the classical view of social cognition as mind-reading, mentalising or maintaining internal rep-resentations of other agents. This project is based on the view that even complex modes of social interaction are grounded in basic sensorimotor interaction patterns. SensoriMotor Contingencies (SMCs) are known to be highly relevant in cognition. Our key hypothesis is that learning and mastery of action-effect contingencies are also critical to enable effective coupling of agents in social contexts. We use “socSMCs” as a shorthand for such socially rele-vant action-effect contingencies. We will investigate socSMCs in human-human and human-robot social interaction scenarios. The main objectives of the project are to elaborate and investigate the concept of socSMCs in terms of information-theoretic and neurocomputational models, to deploy them in the control of humanoid robots (PR2, REEM-C) for social entrainment with humans, to elucidate the mechanisms for sustaining and exercising socSMCs in the human brain, to study their breakdown in patients with autism spectrum disorders, and to benchmark the socSMCs approach in several demonstrator scenarios. Our long term vision is to realize a new socially competent robot technology grounded in novel insights into mechanisms of functional and dysfunctional social behavior, and to test novel aspects and strategies for human-robot interaction and cooperation that can be applied in a multitude of assistive roles relying on highly compact computational solutions.

iNavigate, a Marie-Curie Research and Innovation Staff Exchange (RISE) consortium, brings together scientists and engineers in academic, private and NGO enterprises. Its goal is to promote international and intersectoral cooperation for the next generation, brain-inspired technologies to facilitate the development of intelligent navigation and autonomous mobility solutions. The consortium exploits the complementary competencies of its members while creating synergy through research, innovation, staff exchange and transfer of knowledge. It actively promotes networking, knowledge utilization and dissemination through summer schools, workshops, conferences, and facilitates new skill acquisition and career development in research, innovation and commercialization.

ENRICHME tackles the progressive decline of cognitive capacity in the ageing population proposing an integrated platform for Ambient Assisted Living (AAL) with a mobile service robot for long-term human monitoring and interaction, which helps the elderly to remain independent and active for longer. The system will contribute and build on recent advances in mobile service robotics and AAL, exploiting new non-invasive techniques for physiological and activity monitoring, as well as adaptive Human-Robot Interaction (HRI), to provide services in support to mental fitness and social inclusion. The system will enable caregivers and medical staff to identify evolving trends of cognitive impairments and to detect immediate emergencies. ENRICHME will use new qualitative models for rich yet compact representations of daily life activities. It will also identify humans in order to provide personalized services for elderly living with other persons. Novel context-aware HRI will provide tools for cognitive stimulation and social inclusion, which improve over time by learning from and adapting to the state of the user. A professional infrastructure of networked care will widen the social sphere of intervention in support of elderly and caregivers. ENRICHME includes multi-disciplinary research in geriatrics, gerontology and gero-technology, enabling further studies in social sciences and neuropsychology. Thanks to a modular implementation, which limits costs and allows for maximal flexibility, the system will be tested in 2 separate AAL labs and validated for 1 year in 3 different elderly housing facilities across Europe. Significant impact is expected by prolonging the independent living at home of elderly who need constant monitoring. Their quality of life will be improved by dedicated services for cognitive stimulation and social inclusion. Relevant stakeholders in the project will ensure further impact and economic exploitation of the proposed technologies for healthy ageing.