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 scripts/run-groot.sh from van-i/so100-imitation-learning-stand: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/van-i/so100-imitation-learning-stand/resolve/main/scripts/run-groot.sh
- Command line
-
hf download hf://van-i/so100-imitation-learning-stand/scripts/run-groot.sh
-
curl -L -o run-groot.sh https://huggingface.co/van-i/so100-imitation-learning-stand/resolve/main/scripts/run-groot.sh
1.38 kB
| # Run one GR00T N1.7 trial on the follower. Usage: ./run-groot.sh [checkpoint_step] [seconds] ["task text"] | |
| # ./run-groot.sh -> 018000, 60 s, the trained task | |
| # ./run-groot.sh 012000 30 | |
| # ./run-groot.sh 018000 30 "grab roll and move it to left spot" | |
| # GR00T needs chunked (RTC) inference; the flags below are the ones LeLab uses for it. | |
| set -euo pipefail | |
| STEP=${1:-018000} | |
| DUR=${2:-60} | |
| TASK=${3:-pick r2d2 and put to box} | |
| RUN=$HOME/.cache/huggingface/lerobot/outputs/train/groot_van-i_r2d2_to_box_bg_2026-10-04_14-17-34 | |
| LOG=$HOME/.cache/huggingface/lerobot/inference_logs/$(date +%s)_groot_${STEP}.log | |
| "$(dirname "$0")/camera-setup.sh" >/dev/null | |
| echo "GR00T ${STEP}, ${DUR}s, task: ${TASK} -> ${LOG}" | |
| source "$(dirname "$0")/rollout-common.sh" | |
| run_rollout "$LOG" "$DUR" -- \ | |
| --strategy.type=base \ | |
| --policy.path="$RUN/checkpoints/$STEP/pretrained_model" \ | |
| --policy.device=cuda \ | |
| --inference.type=rtc --inference.rtc.execution_horizon=40 --inference.queue_threshold=0 \ | |
| --robot.type=so101_follower --robot.port=/dev/ttyACM1 --robot.id=so-100 \ | |
| --robot.cameras="{left: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30}, grip: {type: opencv, index_or_path: 4, width: 640, height: 480, fps: 30}, right: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \ | |
| --task="$TASK" \ | |
| --duration="$DUR" | |