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
Update README.md
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README.md
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# LeHome Challenge 2026 — Submission
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**Method**: SmolVLA (frozen four_types 30K backbone) + state-only residual MLP, trained with sparse-reward residual RL on 40 Seen garments. Single model, deterministic inference.
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## Contact
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vita / realvitacai@gmail.com
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---
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datasets:
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- lehome/dataset_challenge
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- lehome/dataset_challenge_merged
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base_model:
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- HuggingFaceTB/SmolVLM2-500M-Video-Instruct
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pipeline_tag: robotics
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tags:
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- robotics
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- lerobot
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- lehome-challenge
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- smolvla
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- residual-rl
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---
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# LeHome Challenge 2026 — Submission
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**Method**: SmolVLA (frozen four_types 30K backbone) + state-only residual MLP, trained with sparse-reward residual RL on 40 Seen garments. Single model, deterministic inference.
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## Contact
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vita / realvitacai@gmail.com
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klein / kleinlau17@gmail.com
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