Papers
16
Total Citations
168
H-Index
8
About
Wei-Yun Yau is a leading researcher in multi-robot systems, reinforcement learning, and autonomous navigation, with a focus on enabling intelligent, cooperative behavior in complex, unknown environments. His major contributions lie at the intersection of multi-agent reinforcement learning (MARL) and robotics, where he has pioneered frameworks for multi-robot search, patrolling, and reliable exploration. Notably, his work on cross-entropy regularized policy gradients and probability density factorized distributional reinforcement learning has advanced the theoretical and practical foundations for non-adversarial moving target search, achieving over 20 citations each. Yau’s research also addresses critical challenges in legged robot navigation, integrating vision and terrain probing for safe traversal, and in graph-based SLAM-aware exploration with prior topo-metric information. His highly cited survey on deep reinforcement learning in mobile robot navigation (30 citations) has become a key reference for the field. With a career spanning from robust hand-eye coordination (1996) to cutting-edge multi-robot active exploration (2024), Yau’s work consistently bridges classical robotics with modern learning-based approaches, earning recognition for its impact on both theory and real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1A Brief Survey: Deep Reinforcement Learning in Mobile Robot Navigation30 citations · 2020
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- 6Graph-Based SLAM-Aware Exploration With Prior Topo-Metric Information12 citations · 2024
- 7Cognitive Navigation for Indoor Environment Using Floorplan9 citations · 2021
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- 9Disentangling Crowd Interactions for Pedestrians Trajectory Prediction8 citations · 2023
- 10Robust hand-eye coordination8 citations · 1996