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. Candidate at the Chair of Robotics, Artificial Intelligence and Real-time Systems. 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 safe reinforcement learning and motion planning, and its application in 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. 


Online Profiles

See Personal Homepage

See Google Scholar

See Github


Research Interests

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

Safe Reinforcement Learning Workshop

We organized a safe reinforcement learning workshop, the researchers and students who are interested in safe RL  are welcome to join us! The recorded videos are available on YouTube's Safe RL Channel, please see the YouTube Channel or  Workshop Homepage.


Offered Thesis Topics

Ongoing Master Thesis Topics:

  • Stability analysis of safe reinforcement learning 
  •  A safe reinforcement learning method based on control theory
  • Privacy Risk Analysis for Synthetic Data

Finished Guided Research topic:

  •  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.


Demos