Papers
9
Total Citations
97
H-Index
5
About
Kong-Wah Wan is a leading researcher in autonomous mobile robotics, specializing in deep reinforcement learning (DRL) for intelligent navigation. His work bridges the gap between conventional, map-dependent path planning and adaptive, learning-based systems that can operate in complex, dynamic environments. Wan’s major contributions include the development of novel DRL architectures, such as the iTD3-CLN and a dueling Twin Delayed DDPG framework, which enable robots to learn collision-free navigation policies directly from raw sensor data. He has also pioneered cognitive navigation models that leverage architectural floorplans for indoor localization, and introduced innovative approaches for crowd navigation using graph convolutional networks to model social interactions. Notably, his research on the emergence of tool use in robots—where machines learn to recognize and employ unfamiliar objects without prior training—showcases his commitment to advancing robotic cognition. With over 90 citations across his most influential works, Wan’s research is widely recognized for its practical impact on service and autonomous robots, pushing the boundaries of how machines perceive, learn, and act in human-centered environments.
Research Focus
Key Achievements
Top Papers
- 1A Brief Survey: Deep Reinforcement Learning in Mobile Robot Navigation30 citations · 2020
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- 4Cognitive Navigation for Indoor Environment Using Floorplan9 citations · 2021
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- 6A Dueling Twin Delayed DDPG Architecture for mobile robot navigation5 citations · 2022
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- 8Partial-Map-Based Monte Carlo Localization in Architectural Floor Plans2 citations · 2021
- 9