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About

David M. Bossens is a robotics and evolutionary computation researcher whose work sits at the intersection of swarm robotics, quality-diversity (QD) algorithms, and resilient autonomous systems. His research addresses one of the central challenges in modern robotics: enabling robots and robot swarms to adapt intelligently to unexpected faults and environmental changes without relying on explicit models. Bossens is perhaps best known for developing the QED (Quality-Environment-Diversity) framework, which applies QD principles to fault recovery in robot swarms — work that has accumulated 21 citations since its 2020 publication. Complementing this, his research on meta-evolution for learning behaviour-performance maps (18 citations) advanced the widely-used MAP-Elites algorithm by automating the often-critical choice of behavior space, a significant methodological contribution to the field. His subsequent work on Quality-Diversity Meta-Evolution extended these ideas to high-dimensional settings through principled dimensionality reduction. Beyond algorithm development, Bossens has contributed a valuable synthesizing review of resilient robot teams, integrating decentralized control, change-detection, and learning into a unified framework. His more recent exploration of heterogeneous multitasking for QD optimization signals a broadening research agenda. Collectively, his contributions meaningfully advance the theory and practice of adaptive, fault-tolerant robotic systems.