Papers
16
Total Citations
257
H-Index
9
About
Xiaoshan Gao is a robotics researcher whose work spans intelligent control, human-robot collaboration, and novel robot design — fields at the cutting edge of autonomous systems and human-machine interaction. With over 220 citations across a decade of scholarship, Gao has established a notable presence in the mobile robotics and reinforcement learning communities. Among Gao's most influential contributions is an improved Deep Deterministic Policy Gradient (DDPG) framework for dynamic obstacle avoidance in mobile robots (2023, 66 citations), which addresses the critical limitation of fixed-obstacle perception in complex, unpredictable environments. Equally impactful is Gao's sustained research into human-robot collaboration, with a hybrid recurrent neural network architecture for intention recognition (2021, 47 citations) building on earlier deep LSTM-based approaches (2019, 24 citations) to enable robots to proactively interpret human motion and intent. Gao's broader portfolio reflects remarkable versatility: from disturbance-rejection-based trajectory tracking for wheeled mobile robots, to magnetic wall-climbing systems, phase-transformable millirobots using magnetorheological liquid metal, and innovative spherical and dual-ball self-balancing robot designs. This breadth — from robust calibration methodologies to soft robotics — positions Gao as a researcher whose contributions meaningfully advance both the theoretical foundations and practical frontiers of modern robotics.
Research Focus
Key Achievements
Top Papers
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- 5Modeling and Analysis of Magnetic Adhesion Module for Wall-climbing Robot17 citations · 2022
- 6Millirobot Based on a Phase-Transformable Magnetorheological Liquid Metal16 citations · 2023
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- 8Design and Modeling of a Dual-Ball Self-Balancing Robot10 citations · 2022
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- 10Calibration method of robot base frame using procrustes analysis5 citations · 2016