SmolVLA — SO101 Multi-Object Packing (Reproduce)
A reproduction of SmolVLA (Hugging Face's ~450M-parameter vision-language-action model) trained with LeRobot on the SO101 robot arm multi-object packing task.
Part of a study reproducing published robot-learning policies to understand and improve on them.
Model details
- Policy: SmolVLA (
smolvla), ~450M parameters, safetensors - Observation: joint state + 3 cameras (
camera1,camera2,camera3) - Action: action chunk of 50 steps (
n_action_steps=50) - License: Apache-2.0
Training
| Setting | Value |
|---|---|
| Dataset | Qiu-Xinchuan/so101_Multi-object-packing |
| Steps | 100,000 |
| Batch size | 8 |
| Learning rate | 1e-4 |
| Seed | 1000 |
Reproduce with LeRobot:
lerobot-train \
--policy.type=smolvla \
--dataset.repo_id=Qiu-Xinchuan/so101_Multi-object-packing \
--steps=100000 \
--batch_size=8 \
--policy.optimizer_lr=1e-4 \
--seed=1000 \
--job_name=smolvla_packing
Usage
lerobot-eval \
--policy.path=Porameht/smolvla_so101_packing_reproduce \
--env.type=... # or run on a real SO101 via lerobot-record / robot control scripts
Load directly:
from lerobot.policies.factory import make_policy
from lerobot.configs.policies import PreTrainedConfig
config = PreTrainedConfig.from_pretrained("Porameht/smolvla_so101_packing_reproduce")
policy = make_policy(config, ds_meta=...) # dataset meta from Qiu-Xinchuan/so101_Multi-object-packing
Evaluation
Not yet evaluated on hardware — success-rate numbers will be added after SO101 rollouts. See the companion ACT reproduction: Porameht/act_so101_packing_reproduce.
Citation
@article{shukor2025smolvla,
title={SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics},
author={Shukor, Mustafa and others},
journal={arXiv preprint arXiv:2506.01844},
year={2025}
}