Papers
6
Total Citations
93
H-Index
4
About
Haoyu Fan is a researcher at the forefront of human-robot interaction and intelligent robotic systems, with a focus on multimodal data fusion and reinforcement learning. His work centers on enabling robots to perceive, decide, and act in complex, dynamic environments—particularly in human-robot confrontation and dexterous manipulation tasks. Fan’s most impactful contribution is an adaptive reinforcement learning-based multimodal data fusion framework for human-robot confrontation gaming, which has garnered 70 citations and addresses key challenges in robot intelligence and anti-interference. He has also advanced finger gesture recognition using sEMG signals and deep learning, and developed a reinforcement learning approach for human-like dexterous grasping in non-visual settings, integrating tactile feedback and force modulation. His research extends to path planning with improved genetic algorithms and virtual simulation platforms for robotic arm education. With a growing citation record and work spanning from 2022 to 2025, Fan is building a reputation for bridging theoretical reinforcement learning with practical, sensor-rich robotic applications, making his research highly relevant for students and engineers working on embodied AI and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 4Path Planning for Mobile Robot Based on Improved Genetic Algorithm4 citations · 2023
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