Summer School on Autonomous Systems

Practical Information

Embodied Intelligence for Safe Autonomous Systems

The program is organized around three core themes:

1. Enhancing efficiency, sustainability, and safety through trustworthy and explainable AI;
2. Enabling the integration of autonomous systems in complex environments;
3. 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 have been exposed to the latest research on autonomous systems and will be able to position their work within the broader ATLAS research framework from a system-level perspective and design high-level architectures 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.