Huiqiao Fu
Papers
5
Total Citations
51
H-Index
4
About
Huiqiao Fu is a leading researcher in legged robotics, specializing in deep reinforcement learning (DRL) for multi-contact motion planning and autonomous navigation in highly unstructured environments. Fu’s major contributions include the development of hierarchical frameworks that combine free gait motion planning with DRL, enabling hexapod robots to traverse complex terrains such as uneven plum-blossom piles and large-scale discrete obstacles. Their most-cited work, “Hierarchical Free Gait Motion Planning for Hexapod Robots Using Deep Reinforcement Learning” (2023, 25 citations), structurally decomposes locomotion into high-level gait selection and low-level foothold optimization, significantly improving robustness. Fu also pioneered a hierarchical multi-expert learning framework inspired by the central nervous system to navigate volatile, uncertain, complex, and ambiguous (VUCA) environments. With over 50 total citations, Fu’s incremental reinforcement learning method (HMC-IRL) further advances the field by enabling robots to adapt to dynamic obstacles. Their notable achievements include bridging classical motion planning with modern DRL, offering scalable solutions for real-world deployment. Fu’s work is essential reading for researchers interested in bio-inspired control, autonomous navigation, and reinforcement learning for complex robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5