Dingsheng Luo
Papers
23
Total Citations
110
H-Index
6
About
Dingsheng Luo is a leading researcher in humanoid robotics, focusing on bio-inspired locomotion, motor skill learning, and human-robot interaction. His work bridges developmental psychology and robotics, particularly in how robots can acquire reaching abilities modeled after human infants—a contribution that has garnered over 10 citations for his 2018 paper on infant-inspired reaching frameworks. Luo’s research on bipedal balance and push recovery, using Dynamical Movement Primitives (12 citations), addresses the critical challenge of maintaining stability in complex environments. He has also advanced gesture recognition for intuitive human-robot interaction (8 citations) and developed methods for online learning of center-of-mass trajectories and foot rolling patterns to achieve more natural, human-like walking. With over 75 total citations across his most-cited works, Luo’s impact is evident in his systematic approach to enabling robots to autonomously learn arm movements, recognize their own activities through multi-sensor fusion, and adapt locomotion in real time. His work not only pushes the boundaries of autonomous motor learning but also offers practical pathways for creating more resilient, human-like robots capable of serving society.
Research Focus
Key Achievements
Top Papers
- 1
- 2An infant-inspired model for robot developing its reaching ability11 citations · 2016
- 3How Does a Robot Develop Its Reaching Ability Like Human Infants Do?10 citations · 2018
- 4
- 5Learning arm movements of target reaching for humanoid robot7 citations · 2015
- 6Online learning of COM trajectory for humanoid robot locomotion7 citations · 2012
- 7Active online learning of the bipedal walking6 citations · 2011
- 8
- 9
- 10Multi-Sensor Fusion Based Robot Self-Activity Recognition4 citations · 2018