Project description
Novel AI technology for dynamic and unpredictable manufacturing environments
Artificial intelligence (AI) systems are increasingly improving the automation of production in the manufacturing sector. But in order for these systems to be trusted and applicable when replacing human tasks in dynamic operation, they need to be safe and adjustable – to react to different situations, security threats, unpredictable events or specific environments. The EU-funded STAR project will rise to this challenge by designing new technologies to enable the implementation of standard-based, secure, safe, reliable and trusted human-centric AI systems in manufacturing environments. The project will aim to research and integrate leading-edge AI technologies like active learning systems, simulated reality systems, explainable AI, human-centric digital twins, advanced reinforcement learning techniques and cyber-defence mechanisms, to allow the safe deployment of sophisticated AI systems in production lines.
Objective
AI systems in industrial plants must be safe, trusted and secure, even when operating in dynamic, unstructured and unpredictable environments. STAR is a joint effort of AI and digital manufacturing experts towards enabling the deployment of standard-based secure, safe reliable and trusted human centric AI systems in manufacturing environments. STAR will research and make available to novel technologies that will enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, it will research technologies that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. STAR’s will research and integration leading edge AI technologies with wide applicability in manufacturing environments, including:
•Active learning systems that boost safety and accelerate the acquisition of knowledge.
•Simulated reality systems that accelerate Reinforcement Learning (RL) in human robot collaboration scenarios.
•Explainable AI (XAI) systems that boost the transparency of industrial systems and increase the trust on them.
•Human Centric digital twins enabling worker monitoring for safer and trustful production processes.
•Advanced RL techniques for optimal navigation of mobile robots and for the detection of safety zones in industrial plants.
•Cyber-defence mechanisms for sophisticated poisoning and evasion attacks against deep neural networks operating over industrial data.
These technologies will be validated in challenging scenarios in manufacturing lines in the areas of quality management, human robot collaboration and AI-based agile manufacturing. STAR will eliminate security and safety barriers against deploying sophisticated AI systems in production lines. The results will be fully integrated into existing EU-wide initiatives (EFFRA, AI4EU), as a means of enabling researchers and the European industry to deploy and leverage advanced AI solutions in production lines.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning reinforcement learning
- social sciences educational sciences pedagogy active learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
RIA - Research and Innovation action
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-ICT-2018-20
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
1050 Bruxelles / Brussel
Belgium
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.