Shaoxiang Li
Papers
3
Total Citations
39
H-Index
3
About
Shaoxiang Li is a robotics researcher whose work sits at the intersection of human-robot interaction, smart healthcare, and sensor-driven automation. His key research areas include service robotics, dynamic movement primitives (DMPs), and multi-modal sensor fusion for robotic manipulation and localization. Li’s most impactful contribution, "An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive" (29 citations), demonstrates how force-sensitive robots can perform adaptive, human-like massage tasks—paving the way for personalized in-home healthcare robots. He further advanced robotic skill transfer with "Development of writing task recombination technology based on DMP segmentation via verbal command for Baxter robot" (7 citations), enabling robots to learn and recombine complex movements from human demonstration using natural language commands. In "Ultra-Wideband Communication and Sensor Fusion Platform for the Purpose of Multi-Perspective Localization" (3 citations), Li explored beacon-based UWB technology to improve multi-robot localization accuracy. His work on the Baxter robot platform and integration of force sensing with DMPs has practical implications for assistive robotics, rehabilitation, and smart home automation. Li’s research is particularly notable for bridging low-level sensor data with high-level task learning, making robots more responsive and intuitive for real-world human environments.
Research Focus
Key Achievements
Top Papers
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