Papers
68
Total Citations
1,353
H-Index
21
About
Kao-Shing Hwang is a prominent robotics and intelligent systems researcher whose work spans mobile robot navigation, visual servoing, reinforcement learning, and multi-robot cooperation. With over 700 cumulative citations across his most influential publications, Hwang has made enduring contributions to the field of autonomous robotic systems over more than two decades. His most celebrated work, a 2019 study on end-to-end deep reinforcement learning for mobile robot navigation (167 citations), tackled the critical sim-to-real transfer gap that limits practical deployment of trained models. Complementing this, his research on image-based visual servoing — including fuzzy adaptive decoupled approaches and collision avoidance for redundant manipulators — has advanced robotic perception and precision control under challenging real-world conditions. His early 1998 work on self-organizing CMAC controllers for trajectory tracking demonstrated a prescient integration of neuro-fuzzy techniques with classical control theory. Hwang has also shaped multi-robot intelligence, developing adaptive Q-learning frameworks for cooperative robot soccer strategy and behavior-based formation control in unstructured environments. His work on biped robot balance via reinforcement learning further illustrates his breadth. Collectively, his research consistently bridges theoretical rigor with practical robotic implementation, making him a significant figure for students exploring autonomous systems and machine learning-driven robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7A Simple Scheme for Formation Control Based on Weighted Behavior Learning55 citations · 2013
- 8Gait Balance and Acceleration of a Biped Robot Based on Q-Learning48 citations · 2016
- 9Collision Avoidance for Redundant Robots in Position-Based Visual Servoing46 citations · 2018
- 10