Apply AI: Robotics for Manufacturing: Advancing Core Skills through Technical Challenges (RIA) (Partnership in AI, Data and Robotics)

Expected Outcome:The Apply AI Strategy highlights the need to accelerate the uptake of AI-powered robotics through sectoral pipelines that connect research and deployment. By developing advanced robotics skills based on foundation models and creating adaptable frameworks that can be transferred across different industrial contexts, including automotive, this topic will provide common tools and building blocks to strengthen those pipelines and ensure broad industrial relevance.Project results are expected to contribute to all of the following expected outcomes:Development of advanced robotics skills (e.g. high precision autonomous pick and place manipulation, autonomous navigation in unstructured environments) using robotics foundation models, tailored for manufacturing. Creation of a comprehensive framework for general purpose and flexible robotics skills development with industry-relevant challenges, evaluation metrics and success criteria.Facilitation of widespread deployment of robotics in manufacturing especially SMEs, through modular, adaptable, and reconfigurable solutions built on robotics foundation models, to meet evolving production needs Scope:The proposed project aims to significantly enhance robotics capabilities in manufacturing by developing advanced robotics skills (for example, task and environment aware autonomous pick and place with high precision and speed, human-robot collaboration, etc).By leveraging the use of next-generation AI, including generative AI, to enable robots to better adapt to real-world environments and interact with human operators, and focusing on reconfigurability, the project will develop industry-agnostic solutions that can be easily adapted to different manufacturing environments.The project will create a comprehensive framework for robotics skills development in manufacturing, including the initial definition of three technical challenges that must be clearly described at proposal stage, with evidence of their industrial r