Gianluca Aguzzi

About

Gianluca Aguzzi is a rising researcher at the intersection of swarm intelligence, collective adaptive systems, and distributed computing, whose work is reshaping how we program and coordinate large-scale autonomous systems. His research centers on field-based computing paradigms, swarm robotics, and multi-agent reinforcement learning, with a consistent focus on enabling emergent, self-organising behaviour in complex cyber-physical ecosystems. Aguzzi's most influential contribution, "A Field-Based Computing Approach to Sensing-Driven Clustering in Robot Swarms" (2022, 14 citations), demonstrates how spatial computing abstractions can elegantly solve coordination problems in large robot collectives. His framework MacroSwarm (2023, 12 citations) further advances compositional swarm programming, offering principled tools for engineering collective behaviours. Complementing this, his work integrating Graph Neural Networks with reinforcement learning (2023, 10 citations) bridges classical distributed computing with modern machine learning, addressing scalability and decentralisation challenges head-on. Across his publications, Aguzzi consistently tackles the fundamental tension between local agent autonomy and globally coherent collective behaviour. His contributions to the ScaFi programming toolkit and pulverised architectures demonstrate both theoretical depth and practical ambition, making his work highly relevant to researchers in robotics, IoT, and edge computing.

Research Focus

Key Achievements

Top Papers

1. 1
   A field-based computing approach to sensing-driven clustering in robot swarms

   14 citations · 2022
2. 2
   MacroSwarm: A Field-Based Compositional Framework for Swarm Programming

   12 citations · 2023
3. 3
   Field-informed Reinforcement Learning of Collective Tasks with Graph Neural Networks

   10 citations · 2023
4. 4
   A Programming Approach to Collective Autonomy

   9 citations · 2021
5. 5
   Self-Organisation Programming: A Functional Reactive Macro Approach

   8 citations · 2023
6. 6
   Programming (and Learning) Self-Adaptive & Self-Organising Behaviour with ScaFi: for Swarms, Edge-Cloud Ecosystems, and More

   6 citations · 2023
7. 7
   Towards Pulverised Architectures for Collective Adaptive Systems through Multi-Tier Programming

   4 citations · 2021
8. 8
   Neighbor-Based Decentralized Training Strategies for Multi-Agent Reinforcement Learning

   3 citations · 2025
9. 9
   Scaling Swarm Coordination with GNNs—How Far Can We Go?

   2 citations · 2025
10. 10
    Software Engineering for Collective Cyber-Physical Ecosystems

    2 citations · 2025

Key Collaborators

Contact & Links