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Lorenzo’s work investigates multi-agent interactive systems, leveraging Multi-Agent Reinforcement Learning and Control to understand how robotic agents can learn and develop diverse capabilities. His curiosity is also directed toward heterogeneity, modular and hierarchical AI, and the practical challenges of implementing these learning systems in autonomous, robust, real-world swarm robotics.