OpenBot

OpenVLA 7B

openvla

OpenVLA 7B (openvla-7b) is an open vision-language-action model trained on 970K robot manipulation episodes from the Open X-Embodiment dataset.

Source notes

OpenVLA 7B (openvla-7b) is an open vision-language-action model trained on 970K robot manipulation episodes from the Open X-Embodiment dataset. The model takes language instructions and camera images as input and generates robot actions. It supports controlling multiple robots out-of-the-box, and can be quickly adapted for new robot domains via (parameter-efficient) fine-tuning.

Code
Unverified
Weights
Available
Checkpoint
Available
License
mit

Decision summary

Best for

VLA training

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
  1. Checkpoint published

    Release timing is recorded from the official source creation date.

    Official source

Selection readiness

Evidence dimensions for deciding whether this model is ready to inspect, compare, or adopt. Scores describe catalog evidence, not task performance.

Published evaluation

How scores work

76/100

Useful

5/6 dimensions scored · 63% confidence

Access and governance75conf. 85
Artifact availability90conf. 90
Training and loading reproducibility70conf. 65
Training data requirements76conf. 70
Evaluation evidenceNot scoredconf. 10
Deployment readiness70conf. 55
Engineering checks (OBRS) behind this record
Engineering checksBronze

Needs Audit

5/12checks passed

OBRS metadata and reported test evidence. A breakdown behind Selection readiness, not a separate score or an independent OpenBot certification.

Standardization & Loaders15 / 25 pt
Physical & Action Quality12 / 25 pt
Semantic & Annotation15 / 20 pt
Real-World Validation0 / 15 pt
License & Compliance10 / 15 pt
Missing evidence and readiness gaps
  • A passing real-hardware test report is required.
  • A passing ingestion/pipeline test report is required.
  • A passing privacy and provenance review is required.

Why these scores

The strongest decision reasons behind Selection readiness.

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.

3 required categories

Observation / ego video

video · image-text-to-text

required

Language intent / task phase

language

required

Action / robot state

actions · feature-extraction

required

Sim-real / embodiment metadata

Descriptive model record

useful

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

No related catalog datasets are linked to this model yet.

More evidenceShow

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

availability
metadata_verified
open
repository metadata
license
metadata_verified
mit
Hugging Face model card license metadata
repository.files
metadata_verified
.gitattributes · README.md · added_tokens.json · config.json · configuration_prismatic.py · generation_config.json · model-00001-of-00003.safetensors · model-00002-of-00003.safetensors · model-00003-of-00003.safetensors · model.safetensors.index.json · modeling_prismatic.py · preprocessor_config.json · processing_prismatic.py · processor_config.json · special_tokens_map.json · tokenizer.json · tokenizer.model · tokenizer_config.json
repository file tree
dataset.signals
official_claim
video · actions · language · feature-extraction · image-text-to-text
README/card declaration
dataset.scale
metadata_verified
label: repository size · value: 15.09GB
repository blob sizes
model.weight_files
metadata_verified
repository: openvla/openvla-7b · count: 3 · sample files: model-00001-of-00003.safetensors · model-00002-of-00003.safetensors · model-00003-of-00003.safetensors
Hugging Face model metadata siblings
model.checkpoint_files
metadata_verified
repository: openvla/openvla-7b · count: 3 · sample files: model-00001-of-00003.safetensors · model-00002-of-00003.safetensors · model-00003-of-00003.safetensors
Hugging Face model metadata checkpoint files
model.config_files
metadata_verified
repository: openvla/openvla-7b · count: 5 · sample files: config.json · generation_config.json · preprocessor_config.json · processor_config.json · tokenizer_config.json
Hugging Face model metadata siblings
model.required_signals
official_claim
video · actions · language · feature-extraction · image-text-to-text
Official model documentation and curated signal mapping
model.training_data
official_claim
dataset: Open X-Embodiment · robot manipulation episodes: 970000
OpenVLA-7B model card, model summary
license.weights
metadata_verified
mit
Hugging Face model card license metadata
release_timing
metadata_verified
2024-06-10T16:35:59.000Z
Existing published Catalog release-date evidence