Shangding Gu, M.Sc.
| shangding.gu(at)tum.de | |
| Address | Boltzmannstr. 3 85748 Garching b. München Gemany |
| Office hours | appointment by email |
Curriculum Vitae
I am a Ph.D. Candidate at the Chair of Robotics, Artificial Intelligence and Real-time Systems, under the supervision of Prof. Alois Knoll. Before studying in Munich, I was a research assistant at the Institute of Automation, Chinese Academy of Sciences, and Tongji University. I had a great time visiting Prof. Jan Peters’s Research Group at the Technical University of Darmstadt from Sep.2022 to Dec.2022. My 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 the 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; 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.
Safe Reinforcement Learning Online Seminar
In December 2022, we launched a long-term safe reinforcement learning online seminar. Every month, we will invite at least one speaker to share cutting-edge research with RL researchers and students (each speaker has about 1 hour to share his/her research). We believe that holding this seminar can promote the research of safe reinforcement learning. For details, please see the Seminar Homepage.
Offered Thesis Topics
- Topic 1: Safe Multi-Agent Reinforcement Learning with Control Theory
- Topic 2: Trusted Reinforcement Learning
- Topic 3: Multi-Robot Navigation
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.