SmolVLA: Efficient Vision-Language-Action Model trained on Lerobot Community Data
SmolVLA: Efficient Vision-Language-Action Model trained on Lerobot Community Data
Hugging Face introduces SmolVLA, a 450M parameter open-source Vision-Language-Action model for robotics, trained on community-shared datasets. It outperforms larger models in simulation and real-world tasks, supports asynchronous inference for faster response, and is designed for deployment on consumer hardware.
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SmolVLA demonstrates that compact, open-source models can outperform larger proprietary systems in both simulation and real-world tasks, highlighting the potential of community-driven data and efficient architectures in advancing robotics research.
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Environment
- SimulationOpen source E1
What remains unknown
Not established in the collected evidence: Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
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SmolVLA-450M outperforms much larger VLAs and strong baselines such as ACT on simulation (LIBERO, Meta-World) and real-world tasks ( SO100, SO101 ).
Open source E1
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Source：Hugging Face — Robotics · huggingface.co