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About

Xuesi Li is a leading researcher in intelligent robotics, specializing in autonomous navigation, fuzzy control systems, and bipedal locomotion. His work bridges the gap between adaptive decision-making and real-world robotic applications, with a particular focus on enhancing robot autonomy in dynamic environments. Li’s most influential contribution is his adaptive decision-making method using fuzzy Bayesian reinforcement learning for robot soccer (60 citations), which integrates probabilistic reasoning with fuzzy logic to improve real-time strategy selection. He also developed a novel fuzzy three-dimensional grid navigation method for mobile robots (13 citations), addressing challenges of complex modeling and computational efficiency in autonomous path planning. His research extends to visual servoing, where he proposed a fuzzy-based approach combined with bagging techniques for wheeled mobile robots (6 citations), enhancing vision-based control robustness. In humanoid robotics, Li has made notable strides in omnidirectional walking and gait planning, introducing fuzzy interpolation methods to improve stability and flexibility in biped robots (5 citations each). His work on inverse kinematics for humanoid robots (2013) and deep neural network-based situation assessment for soccer robots (2019) further demonstrates his versatility. With over 97 total citations, Li’s contributions are pivotal for advancing intelligent, adaptive robotic systems.