Yaning Wang

About

Yaning Wang is a researcher at the intersection of robotics and biomedical engineering, with key contributions in humanoid robot locomotion and autonomous surgical systems. Wang’s early work on the Nao humanoid robot, published in 2012, addressed gait planning using the linear inverted pendulum model (LIPM) and zero moment point (ZMP) criteria, achieving stable, ideal gait patterns through simulation—a foundational step in humanoid robotics with 7 citations. More recently, Wang has pioneered the use of optical coherence tomography (OCT) for real-time tissue sensing in autonomous surgery. In 2022, Wang developed a hybrid MLP-CNN classifier for automated abdominal tissue classification during ventral hernia repair, enhancing surgical precision. This was followed by a 2024 breakthrough introducing a hybrid MLP-DC-CNN classifier for automatic, real-time tissue sensing during intestinal anastomosis, enabling the Smart Tissue Autonomous Robot (STAR) system to perform laparoscopic procedures with improved efficiency and consistency. These contributions, cited 4 times, demonstrate Wang’s impact in advancing autonomous surgical robots toward safer, more reliable operations. Wang’s work bridges robotics and medical imaging, offering transformative potential for minimally invasive surgery.

Research Focus

Key Achievements

Top Papers

1. 1
   Nao humanoid robot gait planning based on the linear inverted pendulum

   7 citations · 2012
2. 2
   Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography

   4 citations · 2024
3. 3
   Automated OCT A-line abdominal tissue classification using a hybrid MLP-CNN classifier during ventral hernia repair

   2 citations · 2022

Key Collaborators

Contact & Links