Papers
12
Total Citations
281
H-Index
7
About
Ling Xu’s research lies at the intersection of robotic grasping, human-robot interaction, and multi-robot coordination, with a focus on making robots more dexterous, robust, and autonomous. Her most influential work introduces **physical human interactive guidance**, a novel method that captures human grasping principles by having people physically guide a robot’s hand. This approach, detailed in her 2012 paper (114 citations), identifies key grasp parameters that humans intuitively optimize, leading to more robust robotic grasping—a result she demonstrated on a physical robot in 2010 (71 citations). Beyond manipulation, Xu has advanced **dynamically-balancing soccer robots**, enabling Segway-based platforms to acquire and use skills for competitive play (31 citations). She has also contributed to **environmental coverage** and **multi-robot planning**, developing efficient graph-based algorithms for tasks like mapping and surveillance (14+ citations each). Her earlier work on a **cerebellum-inspired neural network** for biomorphic robot arms (2006) showcases her interest in biologically motivated control. With over 270 total citations, Xu’s work bridges human insight and robotic capability, offering practical pathways toward more intuitive and capable autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Human-guided grasp measures improve grasp robustness on physical robot71 citations · 2010
- 3Skill Acquisition and Use for a Dynamically-Balancing Soccer Robot31 citations · 2018
- 4Graph planning for environmental coverage14 citations · 2011
- 5An efficient algorithm for environmental coverage with multiple robots14 citations · 2011
- 6Cerebellar Dynamic State Estimation for a Biomorphic Robot Arm9 citations · 2006
- 7Market-based coordination of coupled robot systems7 citations · 2011
- 8Constructive Path Planning for Natural Phenomena Modeling5 citations · 2008
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