Connor Mattson

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About

Connor Mattson is a rising researcher at the intersection of swarm robotics, human-robot interaction, and bio-inspired control. His work focuses on unlocking the potential of large-scale robot collectives, with a particular emphasis on discovering and harnessing emergent behaviors—the complex, unscripted actions that arise from simple agent rules. Mattson’s most cited paper, “Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms” (2023, 5 citations), pioneers a method to efficiently incorporate human intuition into the evolutionary design process, enabling the automatic discovery of a rich taxonomy of collective behaviors. He further advances the field by exploring biologically plausible control in “Spiking Neural Networks as a Controller for Emergent Swarm Agents” (2024, 2 citations), aiming to create truly low-cost, organic-like swarms. In shared autonomy, his 2025 work “Toward Zero-Shot User Intent Recognition” tackles the critical challenge of inferring human goals without prior knowledge, a key step toward seamless human-robot collaboration. Mattson’s research is notable for its ambition to bridge human creativity with algorithmic discovery, promising to make robot swarms more adaptable, intuitive, and accessible for real-world applications.