The EFFECTIVE project foresees the following objectives: • analyse business trends and market prospects, and perform reviews & workshops to get a consistent set of information for clustering and exploitation of FoF projects activities and results; • develop and industrial exploitation framework for the promotion and transfer of FoF projects activities and results, and structure networks & alliances to achieve such objectives; • active clustering of FoF activities and results based the identification of six major technological FoF areas, addressing also potential sectors and applications for exploitation and transfer of the considered clusters of technologies; • analyse best practices and case studies of FoF results clustering, exploitation and transfer through 4 relevant regional case studies in low-, medium- and high-technology intensive sectors, by means of the involvement of innovation actors of the considered regions; • set up instruments for adequate monitoring of FoF activities clustering and exploitation during the project life-time and beyond, to ensure an effective implementation at programme level; • identification of future FoF priorities within the technological areas of the addressed clusters and roadmapping of needed further research and innovation actions; • promote and disseminate the EFFECTIVE project results and tools to a large community of European actors through qualified networks and associations (such as EFFRA, Enterprise Europe Network, EBN and EURADA); • involve international experts and create strategic links with international initiatives to support the full achievement of EFFECTIVE objectives on global scale.

Advances in artificial intelligence (AI) are changing the business models of many companies, and creating entirely new ones. But whereas the general public associates AI predominantly with autonomous and humanoid robots, the economic impact of AI on robotics has been very limited in comparison to domains which were digitised from the start, such as Internet search and social networks. This is because acting in the physical world raises many challenges related to the variability of the real world, its continuous and dynamic nature, as well as the consequences of suboptimal or erroneous behaviour. To address these challenges, VeriDream proposes a two-fold research and innovation strategy for AI in robotics, based on the generalisation and robustification of AI methods developed by the three research partners in two previous H2020 projects, DREAM and RobDREAM. The deep innovation strategy aims at high TRL on a set of use cases from the specific domain of warehouse logistics at the start-up Magazino. The broad innovation strategy, pursued by Synesis and GoodAI, aims at fostering a broader uptake of DREAM methods in SMEs, also beyond the project, and even beyond robotics. VeriDream thus aim at both concrete high-TRL innovation success stories, as well as providing experience and templates for innovation from which other European SMEs may profit. In both strategies, our methodology is based on "closing the innovation loop". This means that research on AI methods is driven less by performance on static benchmarks, but rather by general methodological requirements derived from the performance on multiple industrial use cases. This will require scientific advances in state representation learning, failure discovery and resolution, and continual learning. The generalisation and robustification of DREAM methods that results from this research will have a substantial impact on the innovation potential of these methods. Demonstrating this is VeriDream's mission.