Papers
6
Total Citations
105
H-Index
5
About
Ang Li is a robotics researcher whose work spans force/torque sensing, robot control, and machine learning-based motion planning — areas central to advancing intelligent robotic manipulation in both industrial and space environments. His most cited work, "Design and Calibration of a Six-axis Force/torque Sensor with Large Measurement Range Used for the Space Manipulator" (2015, 58 citations), addresses the formidable engineering challenge of building robust sensors for space robotics, a contribution that has proven foundational for researchers in force-controlled teleoperation. Li has since extended his expertise into adaptive control strategies, including impedance estimation for contact with uncalibrated environments and virtual semi-active damping learning control, enabling manipulators to interact safely with unknown surroundings. His integration of reinforcement learning with Dynamic Movement Primitives for obstacle avoidance (15 citations) demonstrates a forward-looking approach to combining classical motion frameworks with modern learning techniques. More recently, Li has explored visual servoing through both homography-based neural network filtering schemes and end-to-end deep learning controllers, reflecting a commitment to camera-guided, calibration-free robotic systems. Collectively, his work bridges hardware sensing, adaptive control, and learned autonomy — making him a versatile contributor to next-generation robotic manipulation research.
Research Focus
Key Achievements
Top Papers
- 1
- 2Impedance estimation for robot contact with uncalibrated environments16 citations · 2021
- 3
- 4
- 5
- 6