Wenhao Tan
Papers
2
Total Citations
11
H-Index
2
About
Wenhao Tan is a rising researcher in the field of legged robotics, with a primary focus on reinforcement learning for quadruped locomotion. His work addresses two critical challenges in the field: achieving robust, omnidirectional movement across diverse terrains and enabling continuous learning without catastrophic forgetting. In his highly cited 2023 paper, "A Hierarchical Framework for Quadruped Omnidirectional Locomotion Based on Reinforcement Learning," Tan proposed a novel framework that reduces the need for tedious manual tuning and effectively bridges the sim-to-real gap, making deployment in real-world environments more practical. This work has already garnered 8 citations, signaling its impact on the robotics community. Building on this, his 2024 paper, "MCLER: Multi-Critic Continual Learning With Experience Replay for Quadruped Gait Generation," introduces a multi-critic architecture combined with experience replay to allow quadrupeds to learn and retain multiple gaits over time—a significant step toward lifelong learning in robotics. With 3 citations in a short period, this work highlights his innovative approach to overcoming catastrophic forgetting. Tan’s contributions are paving the way for more adaptable and autonomous legged robots, making him a promising voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
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- 2