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About

Shilei Li is a researcher at the forefront of intelligent robotics and human-robot interaction, with a primary focus on state estimation, disturbance rejection, and bio-inspired control for exoskeletal and robotic systems. His major contributions include the development of advanced Kalman filtering techniques, most notably the **Generalized Multikernel Maximum Correntropy Kalman Filter** (35 citations), which robustly estimates disturbances in nonlinear systems, and the **Multi-Kernel Maximum Correntropy Kalman Filter** (19 citations), designed to maintain accurate orientation estimation in inertial measurement units (IMUs) even under external acceleration and magnetic disturbances. These works address critical challenges in real-world applications, from rehabilitation robotics to aerospace. Li has also pioneered bio-inspired methods for real-time human locomotion prediction (9 citations), enabling delay-compensated control of exoskeletal robots. His research extends to autonomous robotic fish navigation and preference-based assistance map learning for lower-limb exoskeletons, demonstrating a versatile impact across both underwater and wearable robotics. With a growing citation record and a clear trajectory toward practical, robust, and human-centered robotic systems, Li’s work is essential reading for students and researchers interested in estimation theory, physical human-robot interaction, and adaptive control.