Papers
12
Total Citations
408
H-Index
9
About
Xiaojing Song is a leading researcher in robotic haptics and tactile sensing, whose work bridges the critical gap between how robots perceive and physically interact with their environment. Her primary research areas include surface material recognition, slip prediction for dexterous grasping, and tactile-based contact shape classification. Song’s major contributions include developing intelligent contact sensing fingers that enable robots to recognize object surface properties through haptic exploration—a foundational capability for autonomous manipulation. Her work on break-away friction ratio and slip prediction has been instrumental in allowing robotic hands to anticipate and compensate for object slippage before it occurs, with her most cited paper on haptic surface exploration accumulating 73 citations. She has also pioneered computationally efficient algorithms for real-time contact shape and pose classification using tactile array sensors, and advanced sensor fusion techniques that combine visual and tactile information for robust 6D object pose estimation. Song’s research has achieved over 396 total citations across her top works, demonstrating significant impact in the robotics community. Her notable achievements include developing Kalman filter-integrated optical flow methods for mobile robot velocity estimation, and exploring the dynamics of passive walking—showcasing her versatility across both manipulation and locomotion domains.
Research Focus
Key Achievements
Top Papers
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- 3Tactile image based contact shape recognition using neural network64 citations · 2012
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- 6Object pose estimation and tracking by fusing visual and tactile information23 citations · 2012
- 7Dominant sources of variability in passive walking18 citations · 2012
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- 9A robust slip estimation method for skid-steered mobile robots9 citations · 2008
- 10