Kai Olav Ellefsen
Serviço Nacional de Aprendizagem Industrial, University of Oslo
Papers
6
Total Citations
102
H-Index
4
About
Kai Olav Ellefsen is a leading researcher in evolutionary robotics and autonomous systems, whose work bridges the gap between simulation and real-world deployment. His primary research areas include multiobjective optimization, evolutionary algorithms, and robotic learning from demonstration. Ellefsen's most impactful contribution is his 2017 paper on multiobjective coverage path planning, which has garnered 57 citations and enables automated inspection of complex, real-world structures—a critical advancement for industrial maintenance and environmental monitoring. He is also known for pioneering work with MAP-Elites algorithms, where his 2018 studies on incremental evolution and dynamic mutation (16–19 citations) have shaped how robots generate diverse gaits and repertoires for locomotion. Notably, his hands-on experience with the DyRET robot platform (2019) demonstrates his commitment to embodied evolution, tackling real-world challenges like hardware variability. More recently, Ellefsen has explored integrating task and motion planning with learning from demonstration (2024) and applying Gaussian processes for robotic environmental monitoring of emission sources (2025), addressing pressing sustainability issues. With a career defined by practical, high-impact solutions, Ellefsen continues to push the boundaries of autonomous robotics in complex, unstructured environments.
Research Focus
Key Achievements
Top Papers
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- 3Dynamic mutation in MAP-Elites for robotic repertoire generation16 citations · 2018
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