Learning-based Methods for Robotics & Autonomous Driving

We focus on developing robotic control and autonomous driving policy learning methods that could directly learn from real-world data, bypassing or alleviating sim-to-real gap, while achieving robust and generalizable performance.

Our current research focus include:

  • Offline RL / IL / planning methods for autonomous driving and robotic control
  • Offline policy optimization for safety-critical scenarios
  • Foundation models for robotic control
  • Sim-to-real adaptation
Xianyuan Zhan
Xianyuan Zhan
Faculty Member