A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. On the other hand, robot manipulation is central to achieve the promise of robotics, since the definition of robot requires that it has actuators that it can use to change the world. In the last decades, a substantial growth has been observed in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create machines capable of directly and autonomously interacting with the world to implement useful applications. Learning will be central to such autonomous systems, as the real world contains too many variations for a robot to have an accurate model of human requests and behaviour, of the surrounding environment, the objects in it, or the skills required to manipulate them, in advance. The main objective of the IntelliMan project is focusing on the question of “How a robot can efficiently learn to manipulate in a purposeful and highly performant way”. IntelliMan will range from learning individual manipulation skills from human demonstration, to learning abstract descriptions of a manipulation task suitable for high-level planning, to discovering an object’s functionality by interacting with it, to guarantee performance and safety. IntelliMan aims at developing a novel AI-Powered Manipulation System with persistent learning capabilities, able to perceive the main characteristics and features of its surrounding by means of a heterogeneous set of sensors, able to decide how to execute a task in an autonomous way and able to detect failures in the task execution in order to request new knowledge through the interaction with humans and the environment. IntelliMan further investigates how such AI-powered manipulation systems are perceived by the users and what factors enhance human acceptability.

This project addresses the scientific, technological and clinical problem of recovery of hand function after amputation. Despite decades of research and development on artificial limbs and neural interfaces, amputees continue to use technology for powered prostheses developed over 40 years ago, namely myoelectric prostheses controlled via superficial electrodes. These devices do not purposely provide sensory feedback and are known for their poor functionality, controllability and sensory feedback, mainly due to the use of surface electrodes. The consortium has pioneered the use of osseointegration as a long-term stable solution for the direct skeletal attachment of limb prostheses. This technology aside from providing an efficient mechanical coupling, which on its own has shown to improve prosthesis functionality and the patient’s quality of life, can also be used as a bidirectional communication interface between implanted electrodes and the prosthetic arm. This is today the most advanced and unique technique for bidirectional neuromuscular interfacing, suited for the upper limb amputees, which was proven functional in the long term. The goal of the DeTOP project is to push the boundaries of this technology –made in Europe– to the next TRL and to make it clinically available to the largest population of upper limb amputees, namely transradial amputees. This objective will be targeted by developing a novel prosthetic hand with improved functionality, smart mechatronic devices for safe implantable technology, and by studying and assessing paradigms for natural control (action) and sensory feedback (perception) of the prosthesis through the implant. The novel technologies and findings will be assessed by three selected patients, implanted in a clinical centre. DeTOP bridges several currently disjointed scientific fields and is therefore critically dependent on the collaboration of engineers, neuroscientists and clinicians.

Collaborative robotics has established itself as a major force in pushing forward highly adaptive and flexible production paradigms in European large and small-medium enterprises. It is contributing to the sustainability and enhancement of Europe’s efficient and competitive manufacturing, to reshoring production, and to economic growth. However, still today the potential of collaborative technologies is largely underexploited. Indeed, collaborative robots are most often designed to coexist and to safely share a working space with humans. They are rarely thought to enter in direct socio-physical contact with humans to perceive, understand, and react to their distress or needs, and to enable them to work more productively and efficiently through better ergonomics. SOPHIA responds to this need by developing a new generation of socially cooperative human-robot systems in agile production. Its modular core technologies will enable dynamic state monitoring of the human-robot pair and anticipatory robot behaviours to: (1) improve human ergonomics, trust in automation, and productivity in manufacturing environments, and (2) achieve a reconfigurable, flexible, and resource-efficient production. By advancing the decisional autonomy and interaction ability of its innovative collaborative systems, SOPHIA will contribute to the reduction of work-related musculoskeletal disorders, the single largest category of work-related injuries and responsible for 30% of all workers’ compensation costs. SOPHIA’s societal relevance and the research groups’ experience in acceptability and standardization aspects of its core technologies will ensure their comfort-of-use by industrial workers, and the underlying design compliance to standards, thus strengthening the competitiveness in European manufacturing. We will illustrate and verify SOPHIA usability through the exploration of three real-world use-cases encouraging potential customers to integrate our core technologies in their workflow.

SYNCLUSIVE is an innovative, integral, and interdisciplinary systems’ approach to stimulate inclusion of vulnerable groups in the labor market. To achieve this, our six central objectives are: 1) developing and consolidating a coalition of stakeholders in 4 regional Living Labs across Europe along the lines of the Community Coalition Action Theory, using the ENGINE approach. This approach includes an integrated package of interventions that stimulates upward and sideward mobility of vulnerable employees, hereby creating vacancies for inflow of vulnerable job seekers; 2) testing the usefulness and applicability of the ENGINE approach for different vulnerable groups identified as being discriminated against; 3) identifying drivers and barriers for mobility and inflow including discrimination; 4) assessing the impact of the implemented ENGINE approach on the labor market mobility and inclusion of vulnerable groups; 5) identifying transition pathways from the regional to the national and EU policy level; and 6) identifying interoperable and comparative indicators and standards that are relevant for the labor market inclusion of vulnerable groups taking into account the regional, national (legislative, social security) and cultural context. Regional stakeholder commitment as well as the interdisciplinary collaboration is crucial for the success of SYNCLUSIVE and for inclusion of vulnerable people in the labor market. SYNCLUSIVE therefore collaborates in an ‘active community coalition’ amongst municipalities, employers, civil society and educational institutes, which interacts with their political counterparts at national and EU level across 7 European regions. SYNCLUSIVE results will lead to sustainable inclusion of vulnerable groups in the regional Living Labs, and have vast potential to result in outcomes far beyond the scope and duration of the project. SYNCLUSIVE will contribute to generic and context-specific EU-wide policy and intervention recommendations that drive inclusion of vulnerable groups and help to start overcoming discrimination in the EU labour market.