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About

Federico Pratissoli is a robotics researcher whose work sits at the intersection of multi-robot systems, distributed control, and autonomous coordination. His research focuses primarily on coverage control algorithms, soft-bodied modular robotics, and machine learning-enhanced multi-robot deployment — areas where he has made notable contributions to both theory and practical implementation. Pratissoli's most influential work addresses a fundamental limitation in classical coverage control: the assumption that robots possess complete environmental knowledge. His 2022 paper on limited-range multi-robot coverage (27 citations) introduced algorithms that operate without global information, making coordination far more practical for real-world deployment. This thread continues through his distributed coverage work for spatial process estimation (7 citations) and time-varying environments (5 citations), culminating in a 2025 federated learning approach that tackles privacy and computational challenges in multi-robot learning. Beyond algorithmic contributions, Pratissoli has explored unconventional robotic morphologies. His research on soft-bodied aggregates of error-prone vibrating modules (13 citations) and the Kilobot Soft Robot (6 citations) demonstrates how reliable, coherent behavior can emerge from mechanically coupled stochastic individuals — a compelling insight bridging swarm intelligence and embodied robotics. His growing citation record reflects increasing recognition across these interconnected fields.