libero_seen_pretrain
Collection
Seen-suite (libero_90) pretraining for LIBERO-Goal few-shot: SmolVLA action BC + action-free latent dynamics (IDM/FDM). • 5 items • Updated
How to use sadjava/smolvla-video-expert-libero90-s1000 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=sadjava/smolvla-video-expert-libero90-s1000 \ --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=sadjava/smolvla-video-expert-libero90-s1000Action-free residual video pretrain of the SmolVLA action expert on RGB from
nvidia/libero_90 (no action / state labels). Predicts SigLIP residual
x_{t+Δ} − x_t (Δ=8). Init: lerobot/smolvla_base. Full action-expert weights (no LoRA).
Use as --policy.path for few-shot LIBERO-Goal FT (OUT_SUFFIX=_frombase_vpre
or _frombase_vlora).
Files:
pretrained_model/ — config + model.safetensors (merged weights)video_expert.json — training hyperparameters (if uploaded)