SmolVLA (LeRobot) SmolVLA is a compact, efficient Vision Language Action (VLA) model designed for affordable robotics, trainable on a single GPU and deployable on consumer hardware, while matching the performance of much larger VLAs through community driven data. Original paper: (SmolVLA: A Vision Language Action Model for Affordable and Efficient Robotics)[https://arxiv.org/abs/2506.01844] Reference implementation: https://github.com/huggingface/lerobot Model description Inputs: images (multi view), proprio/state, optional language instruction Outputs: continuous actions Training objective: flow matching Action representation: continuous Intended use: Base model to fine tune on your specific use case Quick start (inference on a real batch) Installation For full installation details (including optional video dependencies such as ffmpeg for torchcodec), see the official documentation: https://huggingface.co/docs/lerobot/installation Load model + dataset, run select action Training step (loss + backward) If you’re training / fine tuning, you typically call forward(...) to get a loss and then: Notes: Some policies expose policy( batch) or return a dict; keep this snippet aligned with…
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