Junsheng Shi
Papers
2
Total Citations
5
H-Index
2
About
Junsheng Shi’s research lies at the intersection of embodied artificial intelligence and robotic-assisted surgery, with a focus on how machines perceive and interact with the physical world. His most cited work, a 2025 survey on multi-sensor fusion perception (MSFP) for embodied AI, provides a comprehensive roadmap for integrating data from cameras, LiDAR, radar, and other sensors to enable robust 3D object detection and semantic segmentation. This survey, already garnering 3 citations in its first year, addresses critical challenges in autonomous driving and swarm robotics, positioning Shi as a key synthesizer of this rapidly evolving field. Earlier, Shi contributed to medical robotics with a 2017 study on dynamic force modeling for robot-assisted percutaneous operations, using intraoperative data to improve precision during needle insertions. Though less cited, this work demonstrates his ability to translate AI perception principles into high-stakes clinical applications. By bridging the gap between theoretical sensor fusion frameworks and practical surgical robotics, Shi’s research offers valuable insights for students and engineers working to build safer, more perceptive autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2