Instructions to use v1tavitavita/lehome-residual-v4-global with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use v1tavitavita/lehome-residual-v4-global 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=v1tavitavita/lehome-residual-v4-global \ --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=v1tavitavita/lehome-residual-v4-global - Notebooks
- Google Colab
- Kaggle
| { | |
| "config": { | |
| "policy_type": "residual_v4_global", | |
| "policy_class": "ResidualV4GlobalPolicy", | |
| "backbone_path": "/root/gpufree-data/lehome-dev/artifacts/models/smolvla_four_types/checkpoints/030000/pretrained_model", | |
| "backbone_sha256": "7ff3915571622bf7530e9ba35540abf5c14f62d8c6a57491664b65a23869e6bc", | |
| "residual_ckpt_path": "/root/gpufree-data/lehome-dev/artifacts/rl/four_types_sparse_v4/train/residual_averaged.pt", | |
| "residual_ckpt_sha256": "9d695e278b4361509ac7e35f7d66eb251ec7e7f1f7c53878d453ef2b8aa0ce74", | |
| "residual_scale": 0.03, | |
| "task_description": "fold the garment on the table", | |
| "deterministic": true, | |
| "host": "lehome3 / 120.209.70.195:30239" | |
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| }, | |
| "Pant_Short_Seen_6": { | |
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| "success_rate": 1.0, | |
| "exit_code": "0", | |
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| { | |
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| { | |
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| }, | |
| "Pant_Short_Seen_7": { | |
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| "exit_code": "0", | |
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| "Pant_Short_Seen_8": { | |
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| "Pant_Short_Seen_9": { | |
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| { | |
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| { | |
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| }, | |
| "Pant_Short_Unseen_0": { | |
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| "exit_code": "0", | |
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| { | |
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| { | |
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| { | |
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| { | |
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| "length": 366, | |
| "success": true | |
| } | |
| ] | |
| }, | |
| "Pant_Short_Unseen_1": { | |
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| "exit_code": "0", | |
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| { | |
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| { | |
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| ] | |
| } | |
| } | |
| } | |
| }, | |
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| "total_success": 150, | |
| "success_rate": 0.625, | |
| "seen": { | |
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| "success": 136, | |
| "rate": 0.68 | |
| }, | |
| "unseen": { | |
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| "rate": 0.35 | |
| } | |
| } | |
| } |