Yen-Ting Yeh

Papers

Total Citations

H-Index

About

Yen-Ting Yeh is a leading researcher in robotics and artificial intelligence, with a primary focus on bio-inspired control systems and multiobjective optimization for legged locomotion. His most influential work, "Multiobjective Evolution of Biped Robot Gaits Using Advanced Continuous Ant-Colony Optimized Recurrent Neural Networks" (2017, 88 citations), introduces a groundbreaking approach to gait generation for the NAO biped robot. By combining fully connected recurrent neural networks (FCRNNs) as central pattern generators with advanced multiobjective continuous ant colony optimization (AMO-CACO), Yeh pioneered a method that simultaneously optimizes multiple gait performance criteria—such as stability, energy efficiency, and speed—without relying on pre-programmed templates. This work has been widely cited for its novel integration of swarm intelligence with neural control, offering a scalable framework for adaptive locomotion in humanoid robots. Yeh’s contributions are particularly notable for advancing the field of evolutionary robotics, demonstrating how nature-inspired algorithms can solve complex, multi-objective engineering problems. His research continues to influence the development of autonomous, dynamically stable robots capable of navigating unstructured environments.