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

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Original source quotation: “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.”

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Original source quotation: “Original paper: (SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics)[ https://arxiv.org/abs/2506.01844]”

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Original source quotation: “Reference implementation: https://github.com/huggingface/lerobot”

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Original source quotation: “Model description Inputs: images (multi-view), proprio/state, optional language instruction Outputs: continuous actions”

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Original source quotation: “Training objective: flow matching Action representation: continuous Intended use: Base model to fine tune on your specific use case”

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Original source quotation: “Quick start (inference on a real batch) Installation pip install "lerobot[smolvla]" For full installation details (including optional video dependencies such as ffmpeg for torchcodec), see the official documentation: https://huggingface.co/docs/lerobot/installation”

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Original source quotation: “Load model + dataset, run select_action import torch from lerobot.datasets.lerobot_dataset import LeRobotDataset from lerobot.policies.factory import make_pre_post_processors # Swap this import per-policy from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy # load a policy model_id = "lerobot/smolvla_base" # <- swap checkpoint device = torch.device( "cuda" if torch.cuda.is_available() else "cpu" ) policy = SmolVLAPolicy.from_pretrained(model_id).to(device). eval () preprocess, postprocess = make_pre_post_processors( policy.config, model_id, preprocessor_overrides={ "device_processor" : { "device" : str (device)}}, ) # load a lerobotdataset dataset = LeRobotDataset( "lerobot/libero" ) # pick an episode episode_index = 0 # each episode corresponds to a contiguous range of frame indices from_idx = dataset.meta.episodes[ "dataset_from_index" ][episode_index] to_idx = dataset.meta.episodes[ "dataset_to_index" ][episode_index] # get a single frame from that episode (e.g. the first frame) frame_index = from_idx frame = dict (dataset[frame_index]) batch = preprocess(frame) with torch.inference_mode(): pred_action = policy.select_action(frame) # use your policy postprocess, this post process the action # for instance unnormalize the actions, detokenize it etc.. pred_action = postprocess(pred_action)”

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Original source quotation: “Training step (loss + backward) If you’re training / fine-tuning, you typically call forward(...) to get a loss and then: policy.train() batch = dict (dataset[ 0 ]) batch = preprocess(batch) loss, outputs = policy.forward(batch) loss.backward()”

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Original source quotation: “Notes: Some policies expose policy(**batch) or return a dict; keep this snippet aligned with the policy API. Use your trainer script ( lerobot-train ) for full training loops.”

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Original source quotation: “How to train / fine-tune lerobot-train \ --dataset.repo_id= ${HF_USER} /<dataset> \ --output_dir=./outputs/[RUN_NAME] \ --job_name=[RUN_NAME] \ --policy.repo_id= ${HF_USER} /<desired_policy_repo_id> \ --policy.path=lerobot/[BASE_CHECKPOINT] \ --policy.dtype=bfloat16 \ --policy.device=cuda \ --steps=100000 \ --batch_size=4 Add policy-specific flags below: -policy.chunk_size=... -policy.n_action_steps=... -policy.max_action_tokens=... -policy.gradient_checkpointing=true”

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Original source quotation: “Real-World Inference & Evaluation You can use the record script from lerobot-record with a policy checkpoint as input, to run inference and evaluate your policy. For instance, run this command or API example to run inference and record 10 evaluation episodes: lerobot-record \ --robot.type=so100_follower \ --robot.port=/dev/ttyACM1 \ --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \ --robot.id=my_awesome_follower_arm \ --display_data=false \ --dataset.repo_id=${HF_USER}/eval_so100 \ --dataset.single_task="Put lego brick into the transparent box" \ # <- Teleop optional if you want to teleoperate in between episodes \ # --teleop.type=so100_leader \ # --teleop.port=/dev/ttyACM0 \ # --teleop.id=my_awesome_leader_arm \ --policy.path=${HF_USER}/my_policy”

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