VLAOpen

SmolVLA

Hugging Face LeRobot

Compact open vision-language-action policy designed for practical robot fine-tuning and deployment through the LeRobot ecosystem.

Code: Available
Weights: Available
Checkpoint: Available
License: Apache-2.0

Weights Code Model index A–Z

Decision summary

Best for

Data-efficient fine-tuning

Main blocker

Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.

Next check

Run a checkpoint loading smoke test before marking the model pipeline-tested.

Release history

Source-backed release timing for this canonical model record.

Important releases

2024-01-26
Model series introduced
Release timing is recorded from the official source creation date.
LeRobot

Evidence profile

A visual read of adoption evidence. Scores describe catalog evidence readiness, not task performance.

Published evaluation

5/6

dimensions scored

Unknown evidence remains visible and is never treated as a zero.

Access and governance75conf. 85

Artifact availability90conf. 90

Training and loading reproducibility70conf. 65

Training data requirements76conf. 70

Evaluation evidenceNot scoredconf. 10

Deployment readiness70conf. 55

Why these scores

The strongest decision reasons behind the evidence profile.

Top 3 adoption signals

Artifact availability

Useful

Checkpoint files were verified in the official repository metadata.

90 · confidence 90

Training and loading reproducibility

Useful

Configuration files are present; runtime loading is not tested.

70 · confidence 65

Evaluation evidence

Unknown

No structured evaluation evidence has been verified.

— · confidence 10

Data requirements

Declared loop-data needs, missing evidence, and the next checks that matter.

5 required categories

Observation / ego video

observation

required

Language intent / task phase

language intent

required

Action / robot state

actions · robot state

required

Feedback / correction / failure

task success

required

Critical gaps

Core categories are represented. Interface alignment and data quality still require verification.

Linked datasets

Signal-level links only. Verify runtime interfaces before use.

Browse datasets

EgoWorld

Schema validation for LeRobot v3

Useful

provisional fit 75 · confidence 30

2 matched · 2 unknown

EgoStation GoPro Pick-and-Place

Pick-and-place benchmark fixtures

Useful

provisional fit 88 · confidence 45

3 matched · 1 unknown

Egocentric Adjust Bottle

LeRobot conversion tests

Useful

provisional fit 88 · confidence 45

3 matched · 1 unknown

More evidenceShow

Additional data signals

Sim-real / embodiment metadata

robot state

required

Additional decision dimensions

Access and governanceUseful

Access and license are declared by the source.

75 · c85

Training data requirementsUseful

Required signal categories are structured; exact tensor and action interfaces still need verification.

76 · c70

Deployment readinessUseful

Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.

70 · c55

Artifact facts

release_year: official_claim; 2025; curated official source year
model.required_signals: official_claim; observation · language intent · actions · robot state · task success; Official model documentation and curated signal mapping
repository.code: metadata_verified; repository: huggingface/lerobot · default branch: main · archived: false · code file count: 753 · sample files: examples/backward_compatibility/replay.py · examples/dataset/create_progress_videos.py · examples/dataset/load_lerobot_dataset.py · examples/dataset/slurm_compute_rabc.py · examples/dataset/use_dataset_image_transforms.py · examples/dataset/use_dataset_tools.py · examples/isaac_teleop_to_so101/__init__.py · examples/isaac_teleop_to_so101/common.py · examples/isaac_teleop_to_so101/isaac_teleop/__init__.py · examples/isaac_teleop_to_so101/isaac_teleop/base.py · examples/isaac_teleop_to_so101/isaac_teleop/clutch.py · examples/isaac_teleop_to_so101/isaac_teleop/config_isaac_teleop.py · examples/isaac_teleop_to_so101/isaac_teleop/teleop_so101_leader_arm.py · examples/isaac_teleop_to_so101/isaac_teleop/teleop_xr_controller.py · examples/isaac_teleop_to_so101/isaac_teleop/xr_controller_processor.py · examples/isaac_teleop_to_so101/override_reset_pose.py · examples/isaac_teleop_to_so101/record.py · examples/isaac_teleop_to_so101/teleoperate.py · examples/lekiwi/evaluate.py · examples/lekiwi/record.py; GitHub repository metadata and recursive tree
model.hub: official_claim; declared; curated model hub link
model.weight_files: metadata_verified; repository: lerobot/smolvla_base · count: 3 · sample files: model.safetensors · policy_postprocessor_step_0_unnormalizer_processor.safetensors · policy_preprocessor_step_5_normalizer_processor.safetensors; Hugging Face model metadata siblings
model.checkpoint_files: metadata_verified; repository: lerobot/smolvla_base · count: 3 · sample files: model.safetensors · policy_postprocessor_step_0_unnormalizer_processor.safetensors · policy_preprocessor_step_5_normalizer_processor.safetensors; Hugging Face model metadata checkpoint files
model.config_files: metadata_verified; repository: lerobot/smolvla_base · count: 1 · sample files: config.json; Hugging Face model metadata siblings
model.training_data: official_claim; source: LeRobot Community Datasets · curated datasets: 487 · frames: approximately 10 million · frame rate: 30 FPS · primary embodiment: SO100; SmolVLA blog, training dataset
license.code: metadata_verified; Apache-2.0; GitHub repository SPDX license metadata
license: metadata_verified; Apache-2.0; GitHub repository SPDX license metadata
release_timing: metadata_verified; 2024-01-26T15:50:41.000Z; GitHub repository metadata created_at
discovery.source: secondary_claim; name: worldbench/awesome-embodied-data-pyramid · trust: discovery_only · revision: 0568599d0619f20946e8089f6a941d0d9e30b690; #table-embodied-foundation-models-vla-wam, table 17, row 18
discovery.raw_fields: secondary_claim; Time: 2025.6 · Method: SmolVLA · Institution: Hugging Face · Project: [![Website](https://img.shields.io/badge/Website-0A7DBD)](https://huggingface.co/lerobot/smolvla_base) · Model: VLA · Data: ![Real][data-real]; #table-embodied-foundation-models-vla-wam, table 17, row 18
model.reported_release_time: secondary_claim; 2025.6; #table-embodied-foundation-models-vla-wam, table 17, row 18
model.type: secondary_claim; VLA; #table-embodied-foundation-models-vla-wam, table 17, row 18
model.training_data_layers: secondary_claim; real_robot; #table-embodied-foundation-models-vla-wam, table 17, row 18
model.institution: secondary_claim; Hugging Face; #table-embodied-foundation-models-vla-wam, table 17, row 18
catalog.curation: secondary_claim; tier: editorial_focus · collection: WorldBench Awesome Embodied Data Pyramid · policy: human_curated_priority; #table-embodied-foundation-models-vla-wam, table 17, row 18

Additional references

Hugging Face modelModel hub

OpenBot notes

Strong fit for OpenBot's model-readiness view because public weights make the training path concrete.
Catalog should surface whether a dataset can be converted into LeRobot-style episodes.

Related models

smolvla_baseVLA

lerobot

SmolVLA

Decision summary

Release history

Model series introduced

Evidence profile

Why these scores

Artifact availability

Training and loading reproducibility

Evaluation evidence

Data requirements

Observation / ego video

Language intent / task phase

Action / robot state

Feedback / correction / failure

Critical gaps

Linked datasets

EgoWorld

EgoStation GoPro Pick-and-Place

Egocentric Adjust Bottle

Additional data signals

Additional decision dimensions

Artifact facts

Additional references

OpenBot notes

Related models