Shangding Gu, M.Sc.
|Address||Boltzmannstr. 3 |
85748 Garching b. München
|Office hours||appointment by email|
Shangding Gu is a Ph.D. student 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.
- 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
- 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.