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About

Antoine Cully is a pioneering researcher at the intersection of evolutionary computation, robotics, and artificial intelligence, best known for his groundbreaking work on adaptive and resilient robotic systems. His most celebrated contribution, "Robots that can adapt like animals" (2015, 948 citations), introduced the Intelligent Trial and Error algorithm, enabling robots to recover from damage in under two minutes by drawing on a pre-computed behavioral repertoire — a landmark achievement that drew widespread attention across both academia and mainstream science. Cully's research has been central to the development of Quality-Diversity (QD) optimization, a powerful paradigm that generates large collections of diverse, high-performing solutions rather than converging on a single optimum. His work on MAP-Elites and its variants, including policy gradient-assisted approaches, has significantly advanced evolutionary robotics and opened new frontiers in stochastic optimization more broadly. Beyond locomotion, Cully has made meaningful contributions to assistive robotics, particularly personalized robot-assisted dressing for elderly and disabled individuals, demonstrating a commitment to real-world societal impact. Through behavioral repertoire learning and hierarchical repertoire frameworks, he has consistently pushed the boundaries of autonomous robot adaptability, cementing his reputation as a leading voice in intelligent, flexible robotic systems.