- Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings (2024)
- 6IMPOSE: Bridging the reality gap in 6D pose estimation for robotic grasping (2023)
- Flexible Gear Assembly with Visual Servoing and Force Feedback (2023)
- Towards safe ai: Sandboxing dnns-based controllers in stochastic games (2023)
- Cloud-edge training architecture for sim-to-real deep reinforcement learning (2022)
Research Experience
- Present: Ph.D. Student / Research Associate, Chair of Cyber-Physical Systems in Production Engineering, School of Engineering and Design, Technical University of Munich
Education
- PhD: Computer Science, Technical University of Munich, Germany
- MEng: Mechanical Engineering, Zhejiang University, China
- BSc: Mechanical Design and Automation, Shandong University, China
Background
- Research Interests: Robot Learning, Deep Reinforcement Learning, Control Theory
- Professional Field: Intersection of machine learning and control theory, addressing intelligent decision-making problems for autonomous systems
- About Me: Currently a fifth-year Ph.D. student in the Chair of Cyber-Physical Systems in Production Engineering at the Technical University of Munich (TUM), supervised by Prof. Dr. Marco Caccamo.