Shangding Gu, M.Sc.

E-Mail shangding.gu(at)tum.de
Address Boltzmannstr. 3
85748 Garching b. München
Gemany
Office hours appointment by email

Curriculum Vitae

Shangding Gu is a Ph.D. student at the Chair of Robotics, Artificial Intelligence and Real-time Systems, under the supervision of Prof. Alois Knoll. Before studying in Munich, he was a research assistant at the Institute of Automation, Chinese Academy of Sciences, and Tongji University. His research currently focuses on developing artificial intelligence methods and models, with a special interest in exploring the theory of reinforcement learning and motion planning, and its application for robotics, in which his goal is to enable robots to know how to learn, reason and plan, and enable robots to work in support of people. 


Research Interests

  • Safe/Robust Reinforcement Learning; Reinforcement Learning Theory; Optimization Theory.
  • Motion Planning; Autonomous Driving; Robotics (e.g., arm robotics and marine robotics). 

Safe Reinforcement Learning Workshop

We are organizing a safe reinforcement learning workshop, the researchers and students who are interested in safe RL  are welcome to join us! For details, please see the workshop homepage.


Offered Thesis Topics

Ongoing Master Thesis Topics:

  • H. Jaafar (from the mathematics department). Stability analysis of safe reinforcement learning 
  • S. Manxi (from the informatics department). A safe reinforcement learning method based on control theory

Finished Guided Research topic:

  • S. Manxi (from the informatics department). A safe multi-agent reinforcement learning algorithm in robotics applications  

If you are interested in the above topics, please feel free to contact me indicating your background and skills.


Online Profiles

See Google Scholar

See Github


Demos