Xianfeng Tang
Papers
1
Total Citations
10
H-Index
1
About
Xianfeng Tang is a researcher at the forefront of artificial intelligence and robotics, with a primary focus on advancing human motion pose estimation and service robot technology. His most notable contribution is the development of YOLOv8-ApexNet, a novel framework introduced in his highly cited 2024 paper, which addresses persistent limitations in robot pose estimation—a critical challenge for enabling more intuitive human-robot interaction. This work has already garnered 10 citations, signaling its growing influence in the field. Tang’s research bridges the gap between computer vision and practical robotics, aiming to enhance the adaptability and precision of service robots in real-world environments. By exploring new directions in pose estimation, he is helping to unlock the full potential of autonomous systems, from healthcare assistants to industrial automation. His work is particularly impactful for students and researchers seeking to understand how deep learning architectures can be optimized for robotic perception, making him a rising voice in the AI and robotics community.
Research Focus
Key Achievements
Top Papers
- 1