Papers
10
Total Citations
122
H-Index
6
About
Nailong Liu is a leading researcher at the intersection of robotic manipulation, tactile sensing, and intelligent control. His work centers on enabling robots to perceive and interact with their environment with human-like dexterity, spanning artificial skin, vision-based tactile sensors, and learning from demonstration. Liu’s most impactful contribution is the development of a visuo-tactile artificial skin for 3D shape reconstruction, which integrates material design and sensing methodology to provide multifunctional tactile feedback—a breakthrough for dexterous hands and healthcare applications (29 citations). He has also advanced force measurement technology in vision-based tactile sensors, enabling high-precision multimodal force sensing critical for robotic manipulation (27 citations). In assembly tasks, Liu pioneered the use of Cartesian Dynamic Movement Primitives with hybrid force/position feedback for learning peg-in-hole tasks, achieving human-like compliant skills (26 citations). His work on EtherCAT-based modular joint controllers and motion modularity for industrial robots further demonstrates his impact on real-time control and trajectory generation. With over 120 total citations, Liu’s research bridges fundamental sensing and control with practical robotic applications, making him a key figure in modern robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Force Measurement Technology of Vision‐Based Tactile Sensor27 citations · 2024
- 3Learning peg-in-hole assembly using Cartesian DMPs with feedback mechanism26 citations · 2020
- 4EtherCAT based robot modular joint controller12 citations · 2015
- 5
- 6A containerized simulation platform for robot learning peg-in-hole task6 citations · 2018
- 7
- 8Design and Simulation of Spine Rehabilitation Soft Robotic Actuator3 citations · 2019
- 9
- 10A Modified Cartesian Space DMPs Model for Robot Motion Generation3 citations · 2019