🔥 SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

💡 The paper introduces SmolVLA, a compact and efficient vision-language-action model designed for affordable and efficient robotics. The problem with existing vision-language-action models is that they are typically very large, with billions of parameters, which leads to high training costs and limited deployability on consumer-grade hardware. These models also rely on academic and industrial datasets, overlooking community-collected data from affordable robotic platforms.

To address this issue, the authors developed SmolVLA, a small and efficient model that can be trained on a single GPU and deployed on consumer-grade GPUs or even CPUs. SmolVLA is designed to be community-driven, using community-collected data from affordable robotic platforms, which reduces both training and inference costs while retaining competitive performance.

The method used to achieve this involves adapting vision-language models into vision-language-action models, but with a much smaller size. The authors also introduced an asynchronous inference stack that decouples perception and action prediction from action execution, allowing for higher control rates with chunked action generation.

The results show that SmolVLA achieves performance comparable to vision-language-action models that are 10 times larger, despite its compact size. The model is evaluated on a range of simulated and real-world robotic benchmarks, and the authors release all code, pretrained models, and training data. Overall, SmolVLA provides a more efficient and affordable solution for robotics, making it possible to deploy vision-language-action models on consumer-grade hardware.

📅 Published on Jun 2, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2506.01844
• PDF: https://arxiv.org/pdf/2506.01844
• Project Page: https://huggingface.co/blog/smolvla

🤖 Models citing this paper:
• https://huggingface.co/lerobot/smolvla_base
• https://huggingface.co/HuggingFaceVLA/smolvla_libero
• https://huggingface.co/jadechoghari/smolvla_metaworld

📊 Datasets citing this paper:
• https://huggingface.co/datasets/lerobot/community_dataset_v3
• https://huggingface.co/datasets/HuggingFaceVLA/community_dataset_v1
• https://huggingface.co/datasets/HuggingFaceVLA/community_dataset_v2

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/HuggingFaceVLA/libero-vla-leaderboard
• https://huggingface.co/spaces/lerobot/robot-learning-tutorial
• https://huggingface.co/spaces/arpitg1304/lerobot_scripts_simplified

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📢 By: https://xn--r1a.website/PaperNexus

#VisionLanguageAction #EfficientRobotics #CompactNeuralNetworks #RoboticsForAll #AffordableAI