Xiaoqiang Han
Papers
5
Total Citations
34
H-Index
3
About
Xiaoqiang Han is a robotics researcher whose work focuses on enhancing the autonomy and safety of bipedal humanoid robots operating in complex, real-world environments. His primary research areas include bipedal locomotion, balance recovery, and falling motion control, where he has developed human-inspired active compliance and learning-based strategies using Dynamical Movement Primitives. These contributions address critical challenges in robot stability and self-protection, with his most cited paper, "Learning push recovery for a bipedal humanoid robot with Dynamical Movement Primitives" (2015, 12 citations), laying foundational work in adaptive balance control. Han has also explored skill transfer in reinforcement learning through inter-task relations modeled with three-way RBMs, and environmental perception using Gaussian process regression. Demonstrating interdisciplinary reach, his work extends to applied AI in aquaculture, where he co-developed a big-data-based water quality management platform (2020, 9 citations). With a total of 34 citations across his top papers, Han’s research bridges theoretical robotics and practical deployment, contributing to safer, more capable humanoid robots and intelligent systems for real-world applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Biped robot falling motion control with human-inspired active compliance9 citations · 2016
- 3
- 4
- 5Humanoid environmental perception with Gaussian process regression2 citations · 2016