Papers
37
Total Citations
984
H-Index
16
About
Gongfa Li is a prominent robotics and intelligent systems researcher whose work spans dexterous robotic manipulation, human-robot interaction, and machine learning applications in robotics. Based at the intersection of mechanical engineering and artificial intelligence, Li has made significant contributions to advancing robotic hand capabilities in unstructured environments. His most impactful work — a comprehensive survey on deep reinforcement learning, imitation learning, and transfer learning for robotics (222 citations) — has become a key reference for researchers tackling the challenge of intelligent robotic manipulation. Li has also pioneered practical sensing solutions, including a fiber Bragg grating-based three-axis fingertip force sensor (90 citations) and sEMG-driven prosthetic hand systems integrating IoT and haptic feedback (80 citations), bridging cutting-edge technology with real-world assistive applications. Li's contributions extend to grasp planning optimization, inverse kinematics, and semantic scene understanding through CNN-based approaches. His work on facial expression recognition for human-robot interaction further demonstrates his commitment to creating socially aware robotic systems. With over 700 cumulative citations across his published works, Gongfa Li has established himself as a versatile and influential voice in modern robotics research, offering valuable insights for students and engineers advancing intelligent automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2A three-axis force fingertip sensor based on fiber Bragg grating90 citations · 2016
- 3
- 4
- 5
- 6
- 7Optimal grasp planning of multi-fingered robotic hands: a review52 citations · 2015
- 8
- 9
- 10Development of articulated robot trajectory planning22 citations · 2017