Papers
6
Total Citations
87
H-Index
5
About
Jin Pan is a robotics and computer vision researcher whose work spans medical image analysis, autonomous robotic systems, and deep reinforcement learning. He is particularly recognized for his contributions to point set registration, developing sophisticated probabilistic frameworks that account for anisotropic positional errors in 3D alignment tasks — a challenge with direct implications for computer-assisted orthopedic surgery (CAOS), where precise alignment of preoperative and intraoperative scans is critical for patient safety. His most cited work (35 citations) introduced a generalized hybrid mixture model approach that significantly advances registration accuracy in clinical settings. Beyond medical robotics, Pan has tackled real-world automation challenges, including the development of an autonomous airport trolley deployment robot and elevator-navigating robots capable of independent inter-floor movement — both addressing labor-intensive or access-restricted tasks. His 2021 benchmark dataset for elevator button segmentation and character recognition has provided the community with a valuable resource for advancing service robotics research. Pan also explores intelligent exploration strategies through hierarchical deep reinforcement learning, demonstrating a breadth of expertise that bridges theoretical foundations with practical robotic applications. His growing citation record reflects an impactful and versatile research portfolio at the intersection of robotics, computer vision, and surgical intelligence.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6