Papers
4
Total Citations
57
H-Index
3
About
Wei-Kai Chen is a researcher whose work bridges the critical fields of robotic surgery and human-robot interaction, demonstrating a unique ability to tackle both clinical and technical challenges. In surgical robotics, Chen has made significant contributions to gastric cancer treatment, notably through a 2019 study that systematically assessed complications after robotic-assisted total versus distal gastrectomy using the Clavien–Dindo classification. This work, which has garnered 24 citations, provides essential evidence for the safety and feasibility of these advanced procedures, directly informing surgical practice. Beyond the operating room, Chen is a pioneer in robot learning and human intention recognition. By developing frameworks that allow robots to learn pick-and-place tasks from human demonstration—inspired by Gilbreth’s therbligs—and to infer human intentions using Markov decision processes, Chen has advanced the core capabilities needed for intuitive human-robot collaboration. These contributions, cited 17 and 13 times respectively, lay the groundwork for robots that can anticipate and assist human partners in shared tasks. Chen’s research is notable for its dual impact: improving patient outcomes in high-stakes surgery while enabling more fluid and intelligent collaboration between humans and machines.
Research Focus
Key Achievements
Top Papers
- 1
- 2Learning a pick-and-place robot task from human demonstration17 citations · 2013
- 3Human intention recognition using Markov decision processes13 citations · 2014
- 4Active intention inference for robot-human collaboration3 citations · 2017