Robotics
LeRobot
English
lelab
so-100
imitation-learning
education
act
diffusion-policy
smolvla
gr00t
Instructions to use van-i/so100-imitation-learning-stand with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use van-i/so100-imitation-learning-stand with LeRobot:
# See https://github.com/huggingface/lerobot?tab=readme-ov-file#installation for more details git clone https://github.com/huggingface/lerobot.git cd lerobot pip install -e .[smolvla]
# Launch finetuning on your dataset python lerobot/scripts/train.py \ --policy.path=van-i/so100-imitation-learning-stand \ --dataset.repo_id=lerobot/svla_so101_pickplace \ --batch_size=64 \ --steps=20000 \ --output_dir=outputs/train/my_smolvla \ --job_name=my_smolvla_training \ --policy.device=cuda \ --wandb.enable=true
# Run the policy using the record function python -m lerobot.record \ --robot.type=so101_follower \ --robot.port=/dev/ttyACM0 \ # <- Use your port --robot.id=my_blue_follower_arm \ # <- Use your robot id --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras --dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording --dataset.repo_id=HF_USER/dataset_name \ # <- This will be the dataset name on HF Hub --dataset.episode_time_s=50 \ --dataset.num_episodes=10 \ --policy.path=van-i/so100-imitation-learning-stand - Notebooks
- Google Colab
- Kaggle
Download images/train-act-run3.png from van-i/so100-imitation-learning-stand: direct link, hf CLI and curl.
- Browser
- Download file 313 kB
-
https://huggingface.co/van-i/so100-imitation-learning-stand/resolve/main/images/train-act-run3.png
- Command line
-
hf download hf://van-i/so100-imitation-learning-stand/images/train-act-run3.png
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curl -L -o train-act-run3.png https://huggingface.co/van-i/so100-imitation-learning-stand/resolve/main/images/train-act-run3.png
313 kB

- Xet hash:
- 1a7bbb999c8f21819901eb21c7f3cd471d40c9c8cdeff87799dad08f43c6f156
- Size of remote file:
- 313 kB
- SHA256:
- b181321ffb79bc92e5d17823b54eff17b87dedf85a23b9b7379e97455a3e8dce
·
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