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asdl-unist/
smolvla_ER_T3

Name: smolvla_ER_T3
Rating: 2.5 (15 reviews)

asdl-unist/TRAIN_T3_ER

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Model Card for smolvla

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.

This policy has been trained and pushed to the Hub using LeRobot.

Learn how to train and run it in the LeRobot smolvla guide, or browse the full documentation.

Model Details

License: apache-2.0
Fine-tuned from: lerobot/smolvla_base
Robot type: so_follower
Cameras: top, wrist

Inputs & Outputs

The policy consumes these observation features and produces these action features.

Inputs

Feature	Type	Shape
`observation.state`	STATE	`(6,)`
`observation.images.top`	VISUAL	`(3, 480, 640)`
`observation.images.wrist`	VISUAL	`(3, 480, 640)`

Outputs

Feature	Type	Shape
`action`	ACTION	`(6,)`

Training Dataset

Repository: asdl-unist/TRAIN_T3_ER
Episodes: 60
Frames: 15236
Frame rate: 30 FPS
Task(s): "Pick up the left paper cup and stack it onto the right paper cup.", "Pick up the carrot and put it in the basket.", "Place the blue cup into the blue bowl.", "Place the green cup into the green bowl."

Training Configuration

Setting	Value
Training steps	8000
Batch size	16
Optimizer	adamw
Learning rate	0.0001
Seed	1000
LeRobot version	0.6.1

How to Get Started with the Model

New to LeRobot? These guides cover the full workflow:

Install LeRobot — set up the lerobot package.
Hardware setup — assemble, wire, and calibrate your robot and cameras.
Record data & train a policy — the end-to-end imitation-learning walkthrough.
CLI cheat-sheet — quick reference for the lerobot-* commands.

The short version to run and train this policy:

Run the policy on your robot

bash

lerobot-rollout \
  --strategy.type=base \
  --robot.type=so_follower \
  --robot.port=<your_robot_port> \
  --robot.cameras="{ <camera_1>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}, <camera_2>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}}" \
  --policy.path=asdl-unist/smolvla_ER_T3 \
  --task="Pick up the left paper cup and stack it onto the right paper cup." \
  --duration=60

Replace the remaining <...> placeholders with your own values: --robot.port and the camera names/indices are specific to your machine, and the camera names must match the observation keys this policy was trained on.

When --strategy.type=base is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely. For more information look at rollout documentation.

Train your own policy

This policy type is usually fine-tuned from the pretrained base model lerobot/smolvla_base:

bash

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.path=lerobot/smolvla_base \
  --output_dir=outputs/train/<policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<policy_repo_id> \
  --wandb.enable=true

Writes checkpoints to outputs/train/<policy_repo_id>/checkpoints/.

Evaluation

No evaluation results have been provided for this policy yet.

Citation

If you use this policy, please cite the method linked in the description above, along with LeRobot:

bibtex

@misc{cadene2024lerobot,
    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
    title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
    howpublished = "\url{https://github.com/huggingface/lerobot}",
    year = {2024}
}