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Mingxi Intelligent Open Sources MiniCPM-Robot: 1.5B VLA Model Lets Personal Developers Run Real Robots
Until recently, embodied intelligence was largely the domain of large corporations and research labs—models were too big, entry barriers too high, and most developers never got near a physical robot. But on July 19, OpenBMB broke through that barrier. They released the first series of embodied AI models, MiniCPM-Robot, fully open-sourcing a 1.5B parameter vision-language-action model that lets individual developers run operations and target tracking on real robots. Such openness is rare in the field of embodied intelligence.
This series comprises three core components. The first is MiniCPM-RobotManip, a 1.5B parameter general vision-language-action (VLA) model designed for robot manipulation. The second is MiniCPM-RobotTrack, a compact model for real-world target tracking. Additionally, OpenBMB released PhyAI, a high-performance inference framework built specifically for embodied models. Its goal is straightforward: to let robots truly understand, remember, and act. Together, these three components bring efficient, practical, and open embodied intelligence from research papers and demos into actual robots.
For developers, the biggest advantage is easy accessibility. Model weights and code are hosted in the GitHub repository github.com/OpenBMB/MiniCPM-Robot, and the weights for MiniCPM-RobotManip and MiniCPM-RobotTrack are also available separately for download on Hugging Face. Anyone working with robots can now clone and run them directly.
With the 1.5B VLA model no longer confined to the lab, the pace of embodied intelligence could surpass many expectations.
Project repository: https://github.com/OpenBMB/MiniCPM-Robot
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Until recently, embodied intelligence was largely the domain of large corporations and research labs—models were too big, entry barriers too high, and most developers never got near a physical robot. But on July 19, OpenBMB broke through that barrier. They released the first series of embodied AI models, MiniCPM-Robot, fully open-sourcing a 1.5B parameter vision-language-action model that lets individual developers run operations and target tracking on real robots. Such openness is rare in the field of embodied intelligence.
This series comprises three core components. The first is MiniCPM-RobotManip, a 1.5B parameter general vision-language-action (VLA) model designed for robot manipulation. The second is MiniCPM-RobotTrack, a compact model for real-world target tracking. Additionally, OpenBMB released PhyAI, a high-performance inference framework built specifically for embodied models. Its goal is straightforward: to let robots truly understand, remember, and act. Together, these three components bring efficient, practical, and open embodied intelligence from research papers and demos into actual robots.
For developers, the biggest advantage is easy accessibility. Model weights and code are hosted in the GitHub repository github.com/OpenBMB/MiniCPM-Robot, and the weights for MiniCPM-RobotManip and MiniCPM-RobotTrack are also available separately for download on Hugging Face. Anyone working with robots can now clone and run them directly.
With the 1.5B VLA model no longer confined to the lab, the pace of embodied intelligence could surpass many expectations.
Project repository: https://github.com/OpenBMB/MiniCPM-Robot
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