--- base_model: lerobot/smolvla_base datasets: MakotoIto/so101_placeeraser8 library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - robotics - smolvla - lerobot --- # Model Card for smolvla [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. / /resolve/main/demo.gif" width="60%"/> --> This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). Learn how to train and run it in the [LeRobot smolvla guide](https://huggingface.co/docs/lerobot/main/en/smolvla), or browse the [full documentation](https://huggingface.co/docs/lerobot/index). --- ## Model Details - **License:** apache-2.0 - **Fine-tuned from:** [lerobot/smolvla_base](https://huggingface.co/lerobot/smolvla_base) - **Robot type:** `so_follower` - **Cameras:** `top`, `hand` ## Inputs & Outputs The policy consumes these observation features and produces these action features. **Inputs** | Feature | Type | Shape | | --- | --- | --- | | `observation.state` | STATE | `(6,)` | | `observation.images.top` | VISUAL | `(3, 480, 640)` | | `observation.images.hand` | VISUAL | `(3, 480, 640)` | **Outputs** | Feature | Type | Shape | | --- | --- | --- | | `action` | ACTION | `(6,)` | ## Training Dataset - **Repository:** [MakotoIto/so101_placeeraser8](https://huggingface.co/datasets/MakotoIto/so101_placeeraser8) - **Episodes:** 50 - **Frames:** 7018 - **Frame rate:** 15 FPS - **Task(s):** "Place the pink eraser on the white plate" ## Training Configuration | Setting | Value | | --- | --- | | Training steps | 20000 | | Batch size | 64 | | Optimizer | adamw | | Learning rate | 0.0001 | | Seed | 1000 | | LeRobot version | 0.5.2 | --- ## How to Get Started with the Model New to LeRobot? These guides cover the full workflow: - **[Install LeRobot](https://huggingface.co/docs/lerobot/main/en/installation)** — set up the `lerobot` package. - **[Hardware setup](https://huggingface.co/docs/lerobot/main/en/hardware_guide)** — assemble, wire, and calibrate your robot and cameras. - **[Record data & train a policy](https://huggingface.co/docs/lerobot/en/il_robots)** — the end-to-end imitation-learning walkthrough. - **[CLI cheat-sheet](https://huggingface.co/docs/lerobot/main/en/cheat-sheet)** — quick reference for the `lerobot-*` commands. The short version to run and train this policy: ### Run the policy on your robot ```bash lerobot-rollout \ --strategy.type=base \ --robot.type=so_follower \ --robot.port= \ --robot.cameras="{ : {type: opencv, index_or_path: , width: 640, height: 480, fps: 30}, : {type: opencv, index_or_path: , width: 640, height: 480, fps: 30}}" \ --policy.path=MakotoIto/smolvla_placeeraser8 \ --task="Place the pink eraser on the white plate" \ --duration=60 ``` Replace the remaining ` ` placeholders with your own values: `--robot.port` and the camera names/indices are specific to your machine, and the camera names must match the observation keys this policy was trained on. When `--strategy.type=base` is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely. For more information look at [rollout documentation](https://huggingface.co/docs/lerobot/main/en/inference). ### Train your own policy This policy type is usually fine-tuned from the pretrained base model [lerobot/smolvla_base](https://huggingface.co/lerobot/smolvla_base): ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/ \ --policy.path=lerobot/smolvla_base \ --output_dir=outputs/train/ \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/ \ --wandb.enable=true ``` _Writes checkpoints to `outputs/train/ /checkpoints/`._ --- ## Evaluation _No evaluation results have been provided for this policy yet._ --- ## Citation If you use this policy, please cite the method linked in the description above, along with LeRobot: ```bibtex @misc{cadene2024lerobot, author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas}, title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch}, howpublished = "\url{https://github.com/huggingface/lerobot}", year = {2024} } ```
