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About

Paolo Arcaini is a researcher whose work sits at the intersection of autonomous systems, search-based software engineering, and system optimization, with a particular focus on autonomous robots for last-mile goods delivery. His research addresses one of the most pressing challenges in modern logistics: designing, configuring, and validating fleets of autonomous delivery robots that are efficient, sustainable, and safe. Arcaini's major contributions span several complementary directions. He has developed incremental and stability-aware search methods that help stakeholders identify robust system configurations balancing delivery rates, operational costs, and human monitoring requirements. His innovative application of variational autoencoders to constrain search spaces demonstrates a sophisticated integration of machine learning with optimization techniques. On the verification side, his work on metamorphic testing tackles the notoriously difficult oracle problem in testing optimization-based schedulers, offering principled ways to validate systems where correct outputs are hard to define explicitly. With a growing body of work accumulating citations across multiple 2023–2024 publications — including papers with 10 citations each within their first year — Arcaini is establishing himself as an emerging voice in autonomous systems engineering. His research is especially valuable for students and practitioners navigating the complex, multi-stakeholder challenges of real-world autonomous delivery deployment.