Instructions to use IDEAS-Lab-Northwestern/smolvla-base-datagen-v1-clutter-joint-2cam-yanZ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use IDEAS-Lab-Northwestern/smolvla-base-datagen-v1-clutter-joint-2cam-yanZ 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=IDEAS-Lab-Northwestern/smolvla-base-datagen-v1-clutter-joint-2cam-yanZ \ --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=IDEAS-Lab-Northwestern/smolvla-base-datagen-v1-clutter-joint-2cam-yanZ - Notebooks
- Google Colab
- Kaggle
license: apache-2.0
base_model: lerobot/smolvla_base
library_name: lerobot
pipeline_tag: robotics
tags:
- robotics
- vla
- smolvla
- lerobot
- manipulation
- maniguard
- franka
SmolVLA - Clutter (joint, 2-cam)
HuggingFace LeRobot SmolVLA fine-tuned on the ManiGuard clutter base task (sim Franka Panda). Part of the ManiGuard VLA benchmark - SmolVLA vs pi0.5 vs GR00T on the same task families with identical data, cameras, and controller.
Model
- Base: lerobot/smolvla_base - SmolVLM2 vision-language backbone + flow-matching action expert
- Embodiment: Franka Panda, 8-D joint state/action (7 arm joints + 1 gripper), padded to SmolVLA's 32-D
- Cameras (2):
observation.images.top(overview =leftview) +observation.images.wrist(256x256) - Action: absolute joint targets (fed straight to a JointController at eval); 50-step chunk
- Tuning: SmolVLA default - vision encoder frozen, train the action expert (no LoRA)
Training
- 8-GPU accelerate DDP, global batch 512, 3522 steps (~2 epochs over 901,520 frames)
- Data: IDEAS-Lab-Northwestern/datagen-clutter-v1-joint-5cam - the 5-cam datagen dataset, prepared to a 2-cam standard-keyed copy (H.264 videos)
Usage
Load with SmolVLAPolicy.from_pretrained("IDEAS-Lab-Northwestern/smolvla-base-datagen-v1-clutter-joint-2cam-yanZ") from LeRobot. The checkpoint carries the normalization stats.
WARNING - Convention (must match at eval): joint-space JointController (absolute joint targets) + 2 cameras (
observation.images.top= theleftoverview,observation.images.wrist). A mismatched controller, camera set, or overview view silently feeds an out-of-distribution input.