Papers
12
Total Citations
87
H-Index
5
About
Fuda van Diggelen is a robotics researcher whose work sits at the intersection of evolutionary robotics, modular robot systems, and swarm intelligence. His research focuses on some of the field's most pressing challenges: how robots can autonomously learn locomotion, how bodies and brains can co-evolve effectively, and how collective behaviors emerge in robot swarms. Among his most significant contributions is his work on directed locomotion in modular robots with evolvable morphologies, which has accumulated nearly 40 citations across related publications and meaningfully extends classical gait-learning by addressing both movement directionality and morphological diversity. His investigation of the reality gap — the notorious disconnect between simulated and physical robot performance — has helped broaden understanding of this phenomenon beyond fixed morphologies to systems where both controllers and body plans evolve simultaneously. Van Diggelen has also advanced research into swarm robotics, demonstrating how collective and specialized behaviors can emerge without explicit design, even under severe sensing constraints. More recently, his work has pushed toward real-world applicability, including model-free multi-skill learning and the evolutionary codesign of tensegrity joints with artificial muscles. His body of work reflects a researcher steadily bridging theoretical evolutionary computation with practical, deployable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Learning directed locomotion in modular robots with evolvable morphologies26 citations · 2021
- 2Learning Directed Locomotion in Modular Robots with Evolvable Morphologies14 citations · 2020
- 3The Influence of Robot Traits and Evolutionary Dynamics on the Reality Gap13 citations · 2021
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- 5Comparing lifetime learning methods for morphologically evolving robots7 citations · 2021
- 6The Effects of Adaptive Control on Learning Directed Locomotion5 citations · 2020
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