Papers
17
Total Citations
155
H-Index
8
About
Jason Teo is a pioneering researcher whose work sits at the intersection of evolutionary computation, autonomous robotics, and artificial neural networks. His research has made significant contributions to the automatic synthesis of robot controllers, leveraging evolutionary multiobjective optimization (EMO) to solve complex challenges in embodied cognition and locomotion control. Teo's most influential work includes his landmark 2005 paper "Multiobjectivity and Complexity in Embodied Cognition" (33 citations), which introduced a novel framework using EMO to evolve robots with diverse morphologies, fundamentally reshaping how researchers think about artificial organism complexity. His 2008 paper on fast lane detection using Randomized Hough Transform (34 citations) demonstrates his versatility, addressing practical autonomous navigation challenges central to mobile robotics. Across his career, Teo has consistently advanced the application of Pareto-based optimization algorithms — particularly Pareto-frontier Differential Evolution — to evolve neural controllers for legged locomotion, collective robotics, and snake-like modular robots. His review paper "Darwin + Robots = Evolutionary Robotics" further established him as a thoughtful synthesizer of the field's progress and challenges. With over 130 cumulative citations, Teo's body of work provides essential reading for anyone exploring the frontier of evolutionary robotics and intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Fast lane detection with Randomized Hough Transform34 citations · 2008
- 2Multiobjectivity and Complexity in Embodied Cognition33 citations · 2005
- 3Coordination and synchronization of locomotion in a virtual robot12 citations · 2003
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- 9Multi-objectivity as a Tool for Constructing Hierarchical Complexity6 citations · 2003
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