Yi‐Ting Hsieh
Papers
1
Total Citations
23
H-Index
1
About
Yi-Ting Hsieh is a leading researcher at the intersection of robotics, artificial intelligence, and intelligent control systems. Her work focuses on developing adaptive, learning-based frameworks for autonomous locomotion, particularly in humanoid robots. Hsieh’s most notable contribution is the creation of the Fuzzy Double Deep Q-Network (FDDQN), a novel architecture that synergizes the adaptive-network-based fuzzy inference system (ANFIS) with the double deep Q-network (DDQN). This innovation enables humanoid robots to generate stable, efficient gait patterns in real time, addressing a critical challenge in bipedal locomotion. Her seminal 2020 paper on the FDDQN-based gait controller has garnered 23 citations, reflecting its impact on advancing reinforcement learning for physical systems. By blending fuzzy logic’s interpretability with deep reinforcement learning’s power, Hsieh’s work bridges theory and application, offering a scalable solution for adaptive robot control. Her research not only pushes the boundaries of autonomous robotics but also provides a practical framework for engineers seeking to deploy intelligent agents in dynamic, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1