Papers
14
Total Citations
92
H-Index
5
About
Xiaokun Leng is a robotics researcher specializing in bipedal locomotion and dynamic motion control for humanoid and legged robots. Their work bridges model-based planning and learning-based methods to achieve agile, stable, and adaptive behaviors. A key contribution is the development of CDM-MPC, an integrated dynamic planning and control framework that enables bipedal robots to perform acrobatic maneuvers like dynamic jumping (25 citations, 2024). Leng has also advanced walking control through inverted pendulum models for turn-planning (14 citations, 2020) and a universal control framework based on dynamic models and quadratic programming (5 citations, 2020). Their research addresses practical challenges such as uneven terrain walking using centroidal momentum allocation (2023), falling-backward optimization (2020), and multi-objective path planning with artificial potential fields (11 citations, 2018). Leng has explored reinforcement learning for 3D bipedal walking with Fourier series periodic gait planning (2023) and graph-powered motion matching for responsive humanoid motion skills (2023). Their work on dynamic running hexapod robots (2019) and parameter design optimization (2021) further demonstrates a commitment to robust, high-performance locomotion. With over 80 total citations, Leng’s research is shaping the next generation of agile, autonomous legged robots.
Research Focus
Key Achievements
Top Papers
- 1
- 2Inverted pendulum model for turn-planning for biped robot14 citations · 2020
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- 4Dynamic running hexapod robot based on high-performance computing6 citations · 2019
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- 10Uneven Terrain Walking with Linear and Angular Momentum Allocation3 citations · 2023