STAR brings together AI and digital manufacturing experts to deploy standard-based, secure, safe and reliable human-centric AI systems in real-life manufacturing environments. The H2020 project started on the 1st of January 2021, operates on a budget of approximately EUR 6 million and will run for 3 years.

STAR researches, develops, validates and make available to the AI and Industry4.0 communities novel technologies that enable AI systems to take timely and safe decisions in dynamic and unpredictable environments. The project’s results will be fully integrated into existing EU-wide Industry 4.0 and AI initiatives (notably EFFRA and AI4EU), as a means of enabling researchers and the European industry to deploy and fully leverage advanced AI solutions in manufacturing lines. 

STAR project explanatory graphic

To address the challenges of ethical, trusted, and secure AI systems STAR carries out leading-edge AI research and innovation activities in the following areas:

1) Explainable AI: STAR researches and will provide a library of explainable AI (XAI) techniques for manufacturing use cases such as Quality4.0 and human-robot collaboration.

2) Active Learning (AL) and Simulated Reality (SR) for Fast, Safe and Efficient On-Line Learning and Knowledge Acquisition: STAR researches AI systems that operate in dynamic manufacturing environments, while acquiring knowledge in a fast and safe manner. Specifically, STAR researches advanced and efficient forms of Reinforcement Learning (RL), including: (i) Active Learning (AL), approaches that enable robots and other AI systems to query human experts about their next course of action; and (ii) Simulated Reality (SR) approaches as a means of enabling agents to simulate the outcomes of their next action before actually taking it.

3) Human-Centric Digital Twins for Simulation of Safe and Trusted AI Applications with the Human-in-the-Loop: STAR researches and will provide advanced Simulation and Digital Twins solutions for AI-based “human in the loop” processes, including human-robot collaboration.

4) Security for AI systems: STAR will research, implement and validate solutions for securing AI systems in manufacturing, including technologies that address attacks at both the training (i.e., poisoning) and the operational (i.e., evasion) phase of Deep Neural Networks (DNNs). 

STAR logo

Read STAR blog and subscribe to their newsletter and news alerts.

Find STAR on Twitter and LinkedIn.


(Featured photo by Lenny Kuhne on Unsplash)