Emanuele Panizon

The Abdus Salam International Centre for Theoretical Physics (ICTP)

Papers

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About

Emanuele Panizon is a rising researcher at the intersection of swarm robotics, active matter, and reinforcement learning. His work focuses on how groups of simple agents—whether microrobots or biological organisms—can achieve complex collective behaviors through local interactions and learning. Panizon’s most impactful contribution is his 2024 paper on "Counterfactual rewards promote collective transport using individually controlled swarm microrobots" (40 citations), which introduces a novel reinforcement learning framework enabling individual robots to coordinate for tasks like transporting large objects, mimicking the emergent cooperation seen in ant colonies. His 2023 study on "Collective foraging of active particles trained by reinforcement learning" (14 citations) further explores how social interactions can be learned rather than pre-programmed, bridging active matter physics with machine learning. Additionally, his work on "Olfactory search" (2024) systematically reviews algorithmic approaches to odor-source localization, a challenge with applications from animal behavior to chemical hazard detection. By combining theoretical models with practical robotic implementations, Panizon is advancing our understanding of decentralized intelligence, with implications for microrobotics, environmental monitoring, and collective artificial intelligence.