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About

Enbo Li is a robotics researcher whose work focuses on the dynamic control and intelligent manipulation of mobile robots, particularly two-wheeled inverted pendulum (TWIP) systems. His major contributions lie in advancing the stability and autonomy of TWIP robots in challenging, real-world environments. Notably, his 2019 paper on controlling a TWIP robot on continuous uneven ground (7 citations) addressed a critical gap beyond flat-surface operation, using sinusoidal and piece-wise linear functions to model terrain. Li also pioneered the application of reinforcement learning for self-balance control, demonstrating how sequential decision-making can replace traditional controllers. His research extends to mobile manipulation, where he explored target grasping during robot movement (5 citations), and to multi-sensor perception, developing extrinsic calibration methods between stereo systems and 3D LiDAR. With over 19 citations across his most-cited works, Li’s contributions are particularly impactful for students and researchers interested in bridging control theory, machine learning, and practical robotics for outdoor and uneven terrain applications.