Papers
10
Total Citations
184
H-Index
7
About
Xiaolong Wang is a robotics and AI researcher whose work spans humanoid robot control, legged robotics, surgical robotics, and tactile sensing. He has emerged as a prominent figure in embodied intelligence, with his most influential contribution being "Expressive Whole-Body Control for Humanoid Robots" (2024), which has garnered 72 citations and demonstrated how large-scale human motion capture data can train humanoid robots to produce remarkably lifelike, expressive full-body movements. Building on this, his HOVER framework advances versatile whole-body humanoid control across diverse task modes — from navigation to manipulation — reflecting a sustained commitment to general-purpose robot intelligence. Wang has also made meaningful contributions to surgical robotics, with his notch continuum manipulator design for laryngeal surgery attracting 44 citations, bridging precision mechanical design with clinical need. His recent work on vision-language-action models — including NaVILA for legged robot navigation and Helpful DoggyBot for open-world object fetching — positions him at the forefront of integrating large language models with physical robot systems. Complementing these efforts, his research on piezoresistive tactile sensors and thermal flow sensor arrays demonstrates a broad hardware-to-intelligence research philosophy that is shaping the next generation of capable, responsive robots.
Research Focus
Key Achievements
Top Papers
- 1Expressive Whole-Body Control for Humanoid Robots72 citations · 2024
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- 4NaVILA: Legged Robot Vision-Language-Action Model for Navigation13 citations · 2025
- 5HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots12 citations · 2025
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- 9Expressive Whole-Body Control for Humanoid Robots2 citations · 2024
- 10NaVILA: Legged Robot Vision-Language-Action Model for Navigation2 citations · 2024