Xihong Wu
Papers
24
Total Citations
117
H-Index
7
About
Xihong Wu’s research lies at the intersection of developmental robotics, bipedal locomotion, and human-robot interaction, with a focus on endowing humanoid robots with human-like motor skills and balance. Wu’s most significant contributions include pioneering infant-inspired frameworks for robots to autonomously develop reaching abilities, as demonstrated in their highly cited 2016 and 2018 works (11 and 10 citations, respectively). These studies model the embodied emergence of reaching, moving beyond pre-programmed trajectories. In locomotion, Wu has advanced push recovery and falling control using Dynamical Movement Primitives and active compliance (12 and 9 citations), addressing the critical challenge of maintaining balance in complex environments. Their work on online learning of Center of Mass trajectories (7 citations) and visual gesture recognition (8 citations) further showcases a commitment to adaptive, real-world robot learning. With over 80 total citations across a focused body of work, Wu’s research is distinguished by its biologically-inspired approach—translating principles from human infant development and motor control into robust algorithms for humanoid robots. This work has direct implications for creating safer, more capable robots that can learn and operate alongside humans in dynamic settings.
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
- 4Biped robot falling motion control with human-inspired active compliance9 citations · 2016
- 5
- 6Learning arm movements of target reaching for humanoid robot7 citations · 2015
- 7Online learning of COM trajectory for humanoid robot locomotion7 citations · 2012
- 8Active online learning of the bipedal walking6 citations · 2011
- 9
- 10