The REMARO ETN is a consortium of recognized submarine AI experts, software reliability experts, and a marine safety certification agency created to educate 15 ESRs able to realize the vision of reliable autonomy for underwater applications.
REMARO attacks one of the most pressing problems of modern computing, the safety of AI, in the well defined context of submarine robotics. The REMARO research fellows will develop the first ever submarine robotics AI methods with quantified reliability, correctness specifications, models, tests, and analysis & verification methods. REMARO rests on two founding principles: (i) The submarine robot autonomy requires a comprehensive hybrid deliberative architecture, a robotic brain. (ii) Safety and reliability must be co-designed simultaneously with cognition, not separately, as an afterthought. These principles are used to construct the training program (to train ESRs to deliver required scientific breakthroughs) and the expert consortium (to supervise the ESRs, run secondments and courses).
The expertise accumulated in the consortium enables the execution of the interdisciplinary training in (i) computer vision and machine learning, (ii) knowledge, reasoning, and planning, (iii) testing, model-driven-engineering, bug- finding, (iv) verification and model-checking. REMARO delivers a world-class training-by-research to 15 ESRs, with almost 40 days of intense training activities, many interdisciplinary collaborations, 3 cross-sector cross-discipline Challenge Camps, and 37 secondments, including 17 at academic labs and 20 at industrial facilities. The network will communicate results to two large and bustling research communities and to industry via European platforms and its own Industry Follow Group. The training material will be published in the REMARO book and the REMARO online Learning Hub, and the software and data modules will be licensed for open use to accelerate research and maximize the long-lasting impact on European underwater robotics industry.
REMARO has received funding from the European Union’s EU Framework Programme for Research and Innovation Horizon 2020 under Grant Agreement No 956200