Papers
3
Total Citations
30
H-Index
3
About
Simon Carrignon is a researcher whose work sits at the intersection of evolutionary robotics, collective behavior, and embodied cognition. His primary research focus is on how robots can learn and specialize in open, dynamic environments using online, distributed evolutionary methods. Carrignon’s major contribution lies in identifying the critical mechanisms—such as reproductive isolation and population size—that enable behavioral specialization in embodied evolutionary robotics, a notoriously difficult challenge. His most cited work, "Behavioral Specialization in Embodied Evolutionary Robotics: Why So Difficult?" (22 citations), systematically explores these barriers and has become a foundational reference for researchers tackling specialization in multi-robot systems. In a follow-up study, "Benefits of Proportionate Selection in Embodied Evolution" (3 citations), he experimentally demonstrated how fitness-proportionate selection can improve learning outcomes. Carrignon also contributed to the broader dialogue on learning and robotics with his work "Representations to go: learning robotics, learning by robotics" (5 citations). His research is particularly valuable for students and engineers interested in decentralized AI, swarm robotics, and the evolutionary principles that allow robots to adapt without central control.
Research Focus
Key Achievements
Top Papers
- 1
- 2Representations to go: learning robotics, learning by robotics5 citations · 2010
- 3Benefits of proportionate selection in embodied evolution3 citations · 2017