Jialun Lai
Papers
2
Total Citations
10
H-Index
2
About
Jialun Lai is pioneering the intersection of control theory and machine learning for autonomous mobile robotics. Their research focuses on trajectory planning for wheeled vehicles, where they have developed a novel Lyapunov-based reinforcement learning framework that fundamentally addresses critical gaps in data efficiency, safety, and convergence. By integrating Lyapunov stability theory with imitation learning, Lai’s work ensures that learned policies are not only performant but also provably stable—a breakthrough for real-world deployment of autonomous unmanned systems. Their most-cited papers, including "Trajectory planning of mobile robot: A Lyapunov-based reinforcement learning approach with implicit policy" and "A Lyapunov-Based Framework for Trajectory Planning of Wheeled Vehicle Using Imitation Learning" (both 2025, 5 citations each), have already garnered attention for their rigorous theoretical grounding and practical applicability. Lai’s contributions are particularly notable for bridging the gap between classical control guarantees and modern learning-based methods, offering a path toward safer, more reliable autonomous navigation. This work holds significant promise for advancing autonomous systems in logistics, exploration, and service robotics, establishing Lai as an emerging leader in learning-enabled control.
Research Focus
Key Achievements
Top Papers
- 1
- 2