About Me
Hello! Welcome to my homepage! My name is Xiang Meng (孟祥). Currently, I am a final-year Ph.D. Candidate in Humanoid Robotics at Beijing Institute of Technology (BIT), Beijing, China. I received the B.S. degree in Mechatronics Engineering (ME) from BIT in June 2019. From September 2019, I started pursuing the Ph.D. degree with the Intelligent Robotics Institute, School of Mechatronical Engineering, BIT, under the supervision of Prof. Zhangguo Yu (余张国).
My research interests include:
- multi-rigid-body dynamics computational methods;
- nonlinear trajectory optimization;
- linear/nonlinear model predictive locomotion control.
Recently, I have been investigating how to integrate learning-based approaches, such as reinforcement learning (RL) and imitation learning (IL), with model-based motion planning and control frameworks.
My Projects
- Mbot_Model&Control: An introductory tutorial suitable for undergraduate or graduate students new to robotics, providing foundational yet crucial knowledge. Readers can gain insight into fundamental topics including the paradigm of multi-axis robot model construction, Lagrangian derivation of multi-rigid-body dynamics, methods for tuning parameters in current, velocity, and position loops for a single motor, basic model-based control techniques, trajectory planning methods, forward and inverse kinematics solvers, numerical simulation animation programming, and more.
- MXSpatialDynLib: An ultra-lightweight and user-friendly robot dynamics library based on the spatial dynamics algorithm proposed by Roy Featherstone, which includes various matrix computation involving kinematics, dynamics, and centroidal dynamics, facilitating the researches of model-based motion planning and control for legged robots.
- TrajOptim&MPC: A simple and practical tutorial of trajectory optimization (TO) and model predictive control (MPC) for legged robots and systems. This tutorial also serves as an important study material for the Spring 2024/2025 graduate course "Robot System Design and Application" at BIT.
- NLP_AnkTrajOptim4Upstairs: A nonlinear trajectory optimizer for ankle swing trajectory, designed to empower humanoid robots to climb stairs. The nonlinear programming (NLP) is solved by IPOPT.
- TO_Hopper: A TOWR-inspired continuous jumping motion optimization for a one-legged robot. With the direct collocation and the NLP solver fmincon of matlab, the trajectory optimizer can automatically optimize the position of the footholds, the swing trajectories of the foot, the contact forces and the motion trajectory of the floating-base by using the nonlinear hybrid dynamics and the phase-based end-effector constraints.
Publications
- Xiang Meng, Zhangguo Yu, Xuechao Chen, Zelin Huang, Fei Meng, Qiang Huang, Online Adaptive Motion Generation for Humanoid Locomotion on Non-Flat Terrain via Template Behavior Extension, IEEE Transactions on Automation and Science Engineering (T-ASE), 21(4): 6563-6574, 2023. (SCI Top, IF: 5.9, Presented in IEEE ICRA 2024)
- Xiang Meng, Zhangguo Yu, Xuechao Chen, Zelin Huang, Chencheng Dong, Fei Meng, Online Running-Gait Generation for Bipedal Robots with Smooth State Switching and Accurate Speed Tracking, Biomimetics, 8(1): 114, 2023. (SCI, IF: 4.5, Selected as Journal Title Story)
- Xiang Meng, et al. Optimization of Whole-Body Motion for Humanoid Robot Walking Down Stairs with Small Joint Range of Motion, IFToMM World Congress 2023 on Mechanism and Machine Science, pp. 859-868, 2023. (EI, oral)
- Xiang Meng, Zhangguo Yu, Gao Huang, Xuechao Chen, Weijie Qi, Qiang Huang, Bo Su, Walking Control of Biped Robots on Uneven Terrains Based on SLIP Model, 2019 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO), pp. 174-179, 2019. (EI, oral)
Internships
- 2023.08-2023.11, Qiyuan Lab, Beijing.
Services
- Conference Reviewer: RSS 2024, IEEE BioRob 2024, IEEE/RSJ IROS 2025
- Journal Reviewer: T-ASE, RA-L, RAS, Advanced Robotics, Biomimetics
Contact Me
- Email: 3120215100@bit.edu.cn, 750996906@qq.com
- Google Scholar: Xiang Meng
- Github: mx0922
- ORCID: Xiang Meng