Changsheng Hao

Changsheng Hao

Master at Lund University

About

I am Changsheng Hao, currently a Master’s student in Machine Learning, Systems and Control at Lund University (expected June 2026). I received my Bachelor of Engineering in Automation from Shanghai Jiao Tong University in 2023.

My research focuses on locomotion control for legged robotic systems, with particular interest in model-based control, nonlinear optimal control, and whole-body control. I am especially interested in understanding how mechanical design, actuation, and control architecture interact to determine system-level performance.

More broadly, I am motivated by the development of robust and safe control strategies for dynamic robots, and I aim to explore the integration of predictive control methods (e.g., NMPC, MPC) with learning-based approaches for improved adaptability and stability.

This website presents selected projects and research experiences, including locomotion controller implementation, robotic software system design, and real-world hardware validation.

Education

  • Technical University of Munich, Munich, Germany (2025)
    Exchange semester at School of Informatics

  • Lund University, Lund, Sweden (2024 – 2026, expected)
    Master’s Programme in Machine Learning, Systems and Control
    Current GPA: 4.67 / 5

  • Shanghai Jiao Tong University, Shanghai, China (2019 – 2023)
    Bachelor of Engineering in Automation

Research Experience

  • Institute for AI Industry Research, Tsinghua University, Beijing, China
    Research Intern – Locomotion Controller Developer (May 2024)

    Worked on locomotion control algorithms for legged robotic systems, focusing on controller implementation and experimental validation.

Honors & Awards

  • National Champion (1 / 203) – RoboMaster University Championship (RMUC), 2021
  • 1st Prize (24 / 231) – RoboMaster University Championship (RMUC), 2022
  • 1st Prize (National Rank No.2) – RoboCup China Robot Competition, Smart Car Challenge (1:12 Group), 2022
  • Honorable Mention (Top 30%) – Mathematical Contest in Modeling, 2021

Technical Skills

Programming Languages
C, C++, Python, Julia

Frameworks & Libraries
ROS, KDL, OCS2, Pinocchio, PyTorch

Control & Optimization
MPC, NMPC, LQR, PID

Hardware Experience
STM32, IMU, TOF, LiDAR, visual-tactile sensors

Research Interests

  • Nonlinear Model Predictive Control for legged robots
  • Whole-Body Control and dynamic locomotion
  • Safety and robustness in learning-based control
  • Integration of control theory and reinforcement learning