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GO-1-Pro: Is Diversity All You Need for Scalable Robotic Manipulation?
The first comprehensive analysis of data diversity principles revealing optimal scaling strategies for large-scale robotic manipulation training.

2026.02.05
We are proud to recognize our members for their outstanding achievements in 2025.
2026.02.10
2026.02.05
2026.01.19
We are searching for talents from all over the world. Are you looking for opportunities? Don't hesitate to contact us via [email protected] or Dr. Hongyang Li.
- 具身智能研究員 / 自動駕駛研究員 / 機器人硬件工程師 / 科研助理 / 生態合作助理 【更多詳情】
- Ph.D. student / Research Assistant / Postdoc / etc. in Hong Kong and Shanghai
- Full-time employee and Intern (international are welcome)
Representative work published at top-tiered venues.
Preprint 2026Position Paper
UniAD: The first comprehensive framework that incorporates full-stack driving tasks.
A novel generalist policy that leverages latent action representations to maximize data utilization, demonstrating predictable performance scaling with increased data volume.
In this survey, we provide a comprehensive analysis of more than 270 papers on the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving.




