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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.
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)
Embodied AI
End-to-End
Autonomous Driving
Representative work published at top-tiered venues.
Preprint 2026Position Paper
The first comprehensive analysis of data diversity principles revealing optimal scaling strategies for large-scale robotic manipulation training.
arXiv 2025
A unified vision-language-action framework that enables policy learning across different environments.
RSS 2025
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.
IROS 2025 Best Paper Finalist
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.
IEEE-TPAMI 2024Survey
AgiBot WorldManipulation
Manipulation- Cutting-edge Sensor and Hardware Design.
- Wide-spectrum of Scenario Coverage.
- Quality Assurance with Human-in-the-loop.
OpenDV
GenerationWorld Model
GenerationWorld Model- The largest driving video dataset to date, containing more than 1700 hours of real-world driving videos.
Challenge 2025
Autonomous Grand Challenge
