Published multiple high-level papers, including but not limited to works in T-RO'25, T-MECH'25, T-CCN'25.
Research Experience
1. NeuPAN: A direct point-to-point robot navigation framework using end-to-end model-based learning.<br>2. EARN: An edge-accelerated robot navigation system that achieves real-time navigation for low-cost robots through collaborative motion planning.<br>3. GSMR: Gaussian Splatting Mixed Reality technology aimed at reducing the amount of image transmission to lower communication overheads.
Background
Focused on general autonomous navigation of mobile robotics, with an emphasis on leveraging high-dimensional optimization and high-fidelity simulation to enhance their efficiency and robustness.
Miscellany
The lab is currently recruiting MPhil and Ph.D. students for research areas such as LiDAR SLAM, planning, simulation, optimization, and reinforcement learning.