REliable & eXplAinable Swarm Intelligence for People with Reduced mObility: REXASI-PRO

Programme(s): HORIZON.2.4 - Digital, Industry and Space 

HORIZON.2.4.5 - Artificial Intelligence and Robotics

Topic: HORIZON-CL4-2021-HUMAN-01-01 - Verifiable robustness, energy efficiency and transparency for Trustworthy AI: Scientific excellence boosting industrial competitiveness (AI, Data and Robotics Partnership) (RIA)

Call for proposal: HORIZON-CL4-2021-HUMAN-01

Funding Scheme: HORIZON-AG - HORIZON Action Grant Budget-Based

Grant agreement ID: 101070028

Objective: The REXASI-PRO project aims to release a novel engineering framework. The REXASI-PRO project aims to release a novel engineering framework to develop greener and Trustworthy Artificial Intelligence solutions. In the methodology, safety, security, and explainability are entangled. In addition, throughout the entire lifecycle of the framework, ethics aspects will be continuously monitored. To this end, the REXASI-PRO project introduces several novelties. The project will develop in parallel the design of novel trustworthy-by-construction solutions for social navigations and a methodology to certify the robustness of AI-based autonomous vehicles for people with reduced mobility. The trustworthy-by-construction social navigation algorithms will exploit mathematical models of social robots. The robots will be trained by using both implicit and explicit communication. REXASI-PRO methodology augments existing system-level and item-level engineering frameworks by leveraging novel eXplainability methods to improve the entire system's robustness. REXASIPRO will release additional verification and validation approaches for safety and security with the AI in the loop. Among the other developments, a novel learning paradigm embeds safety requirements in Deep Neural Network for planning algorithms, runtime monitoring based on conformal prediction regions, trustable sensing, and secure communication. The methodology will be used to certify the robustness of both autonomous wheelchairs and flying robots. The flying robots will be equipped with unbiased machine learning solutions for people detection that will be reliable also in an emergency. Thus, REXASI-PRO will make the AI solutions greener. To this end, both an AI-based orchestrator to augment the intelligence of the robots and topological methods will be developed. The REXASI-PRO framework will be demonstrated by enabling the collaboration among autonomous wheelchairs and flying robots to help people with reduced mobility.