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 relevance and potential impact. The detailed specification and design of these challenges may be further refined during the first phase of the project in collaboration with industry partners.

The project will organize a multi-stage competition for each of the three identified technical challenges. Each stage of the competition is expected to present an increased level of complexity compared to the previous one. The approach for designing the competitive process, including the use of FSTP, should aim at maximising the impact.

One of the key use cases for this project will be the automotive industry, which should be explicitly included in proposals either as a primary focus or as a dedicated use case, demonstrating how advanced robotics can enhance production efficiency and adaptability in this sector. Other use cases alongside the automotive one are allowed and encouraged, to demonstrate the industry-agnostic nature and the transferability of the developed solutions to different industrial contexts.

User-industry companies from the manufacturing sector (including automotive) should be core partners in the consortium. They should demonstrate a genuine interest in the project results and actively support the FSTP recipients in achieving powerful and exploitable results that benefit their industry.

Organization of the Challenge:

Stage 1 - Open call: The consortium launches an open call for proposals. A challenge, open to all, will allow the selection of the 10 highest-ranked proposals for each of the three technical robotics skills, according to a pre-defined selection process and criteria. Each solution competing for the challenge can be submitted either by a single SME, research organisation or public body secondary or higher education establishment, developer of robotics solutions, or a small team of organizations.

Stage 2 - Competition among Stage 1 winners: The 10 teams or organisations selected from Stage 1 will receive a EUR 200,000 FSTP grant each in accordance with their successfully selected proposal (which addresses the tasks and challenges defined for this stage by the consortium). At the end of Stage 2, the 3 highest-ranked competing solutions will be selected for the next stage according to a pre-defined selection process and criteria.

Stage 3 - Grand Finale (competition among Stage 2 winners): The 3 best teams or organisations selected from Stage 2 will receive a EUR 1,000,000 FSTP grant each in accordance with their successfully selected proposals to address the tasks and challenges defined for this stage. In conjunction, they will prepare for the grand finale that will identify the best performing solution at the end of Stage 3 according to the evaluation methodology defined by the consortium.

The consortium should define measures to support the team winning the grand finale in maximising the impact and uptake of its solutions.

Proposals must include a draft exploitation plan outlining how the solutions developed by the FSTP recipients will be taken up, with concrete support from the user-industry partners to ensure industrial relevance and future exploitation.

This scheme is repeated for each of the three technical challenges.

The consortium will ensure high visibility of the competitions, including possible sponsorships, and will seek to attract the best developers from the EU and associated countries to compete, particularly SMEs, alone or within a team competing for the challenges.

All proposals are expected to incorporate mechanisms for assessing and demonstrating progress, including qualitative and quantitative KPIs, benchmarking, and progress monitoring. This should include the methodology to accompany the challenge participants to the various stages during the project and the assessment methodology during the various selection stages.

When possible, proposals should build on and reuse public results from relevant previous funded actions. Communicable results should be shared with the European R&D community through the AI-on-demand platform and, if necessary, other relevant digital resource platforms to bolster the European AI, Data, and Robotics ecosystem by disseminating results and best practices.

This topic implements the co-programmed European Partnership on AI, data, and robotics (ADRA), and all proposals are expected to allocate tasks for cohesion activities with ADRA

Proposals should also build on or seek collaboration with relevant projects and develop synergies with other relevant International, European, national, or regional initiatives.

Technology Readiness Level - Technology readiness level expected from completed projects

Activities are expected to start at TRL 2 and achieve TRL 5 by the end of the project - see General Annex B.