Chenxuan Li
Papers
2
Total Citations
16
H-Index
2
About
Chenxuan Li is a leading researcher in robot manipulation and embodied intelligence, with a focus on advancing multi-embodiment learning through large-scale, high-quality datasets. Their most significant contribution is the development of **RoboMIND** (Multi-embodiment Intelligence Normative Data for Robot Manipulation), a groundbreaking benchmark that has rapidly garnered over 14 citations since its 2025 release. This dataset comprises **107,000 demonstration trajectories** spanning **479 diverse tasks** and **96 object classes**, collected via human teleoperation to ensure rich, real-world variability. RoboMIND addresses a critical bottleneck in robotics: the lack of standardized, multi-embodiment training data. By providing comprehensive, normative data, Li’s work enables more robust and generalizable robot learning across different hardware platforms. This benchmark is poised to become a foundational resource for the field, accelerating progress toward dexterous, adaptable robotic systems. Li’s research directly supports the next generation of autonomous manipulation, making them a key figure in the push for scalable, data-driven embodied AI.
Research Focus
Key Achievements
Top Papers
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