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About

Robert Hunjet is a researcher specializing in swarm robotics, human-swarm interaction, and autonomous systems, with particular expertise in the shepherding problem and UAV swarm intelligence. His work sits at a compelling intersection of multi-agent systems, machine learning, and defence applications. Hunjet's most influential contribution lies in shepherding research — exploring how autonomous agents can guide and control swarms toward goals without direct communication. His most-cited paper, "The Limits of Reactive Shepherding Approaches for Swarm Guidance" (2020, 26 citations), rigorously maps the boundaries of existing herding algorithms, while complementary works on force vector modulation and UxV shepherding extend these insights toward practical human-swarm teaming scenarios. His 2022 paper applying actor-critic deep reinforcement learning to collective motion tuning (19 citations) demonstrates a forward-looking embrace of modern AI techniques. Beyond shepherding, Hunjet has made notable contributions to UAV swarm communications in contested tactical environments, developing adaptive data ferrying methods using hyper-heuristic policy evolution. Across more than ten publications accumulating over 100 citations, his research consistently bridges theoretical swarm dynamics with real-world defence and robotics applications, making him a distinctive voice in the autonomous systems research community.