Faculty of Science, Technology and Medicine
Faculty of Law, Economics and Finance
Faculty of Humanities, Education and Social Sciences
Interdisciplinary Centre for Security, Reliability and Trust
Luxembourg Centre for Systems Biomedicine
Luxembourg Centre for Contemporary and Digital History
Luxembourg Centre for European Law
Luxembourg Centre for Socio-Environmental SystemsSummer School on Autonomous Systems
This summer school welcomes doctoral candidates and early-stage researchers in autonomous systems, robotics, artificial intelligence, and cyber-physical systems, providing advanced interdisciplinary training at the intersection of Digital Twin technologies, embodied and physical AI, distributed and data-driven intelligence, as well as safety and trustworthiness in autonomous systems.
Practical Information
Embodied Intelligence for Safe Autonomous Systems
The programme is organised around three core themes: enhancing efficiency, sustainability, and safety through digital twins and AI; enabling the integration of autonomous systems in complex environments; and advancing data-driven and distributed AI approaches for autonomous systems. It addresses the inherently physical nature of autonomy across land systems (intelligent transport and infrastructure), air systems (UAVs and aerial mobility), and space systems (robotics and satellites). A central premise of the Summer School is that autonomy is fundamentally embodied: intelligence arises from the closed-loop interaction between perception, actuation, physical dynamics, and environmental constraints.
Expected Learning Outcomes
Upon completion of the Summer School, participants will be able to position their research within the broader ATLAS research framework from a system-level perspective and design high-level architectures for Digital Twins of embodied autonomous systems. They will be trained to integrate sensing, perception, decision-making, and actuation within coherent AI-driven autonomous system architectures, and to apply distributed and federated learning approaches in networked and decentralized autonomous systems. Participants will also be able to consider safety constraints and explainability principles into system architectures, formulate cross-domain research concepts spanning Land, Air, and Space, and critically assess simulation-to-real gaps and validation strategies for physical AI systems.