Papers
6
Total Citations
135
H-Index
4
About
Cheng Ding is a robotics researcher whose work lies at the intersection of brain-computer interfaces, industrial automation, and intuitive robot programming. His most impactful contribution is a shared control framework for robotic arms that combines non-invasive EEG-based brain-computer interfaces with computer vision guidance, a paper that has garnered 103 citations and demonstrates a pioneering approach to assistive robotics. In industrial robotics, Ding has advanced sensor calibration with a method for *in situ* calibration of six-axis force-torque sensors on robots with tilting bases, addressing the critical need for maintaining sensor accuracy without specialized external equipment. His research also focuses on making industrial robots more accessible through reconfigurable pick-and-place systems and template-based imitation learning for manipulating symmetric objects. Notably, Ding has developed a feature-reserved teaching method for pick-and-place systems and a single-demonstration approach for peg-in-hole assembly programming, both aimed at reducing the time and expertise required for robot reprogramming in flexible manufacturing. Through these contributions, Ding is bridging the gap between advanced robotics and practical, user-friendly industrial applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3A Reconfigurable Pick-Place System Under Robot Operating System5 citations · 2018
- 4Template-based imitation learning for manipulating symmetric objects4 citations · 2021
- 5
- 6Peg-in-Hole Assembly Programming Through a Single Demonstration2 citations · 2025