Octo
Octo Model Team
Open-source generalist robot policy pretrained on Open X-Embodiment trajectories and designed for fine-tuning to new robots and tasks.
- Code
- Not listed
- Weights
- Available
- Checkpoint
- Available
- License
- mit
Decision summary
Fine-tuning to new observation spaces
Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.
Verify weight files, sizes, format, and loading instructions.
Release history
Source-backed release timing for this canonical model record.
- Paper
Model series introduced
Release timing is anchored to the cited paper publication date.
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.
Why these scores
The strongest decision reasons behind the evidence profile.
Artifact availability
UsefulArtifact availability is declared by the published record; exact files and loadability still require metadata verification.
Training and loading reproducibility
UsefulConfiguration files are present; runtime loading is not tested.
Evaluation evidence
UnknownNo structured evaluation evidence has been verified.
Data requirements
Declared loop-data needs, missing evidence, and the next checks that matter.
Observation / ego video
observation · goal image
Language intent / task phase
language intent · goal image
Action / robot state
actions · robot state
Sim-real / embodiment metadata
robot state
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.
EgoWorld
Schema validation for LeRobot v3
provisional fit 83 · confidence 40
2 matched · 1 unknown
Egocentric Adjust Bottle
LeRobot conversion tests
provisional fit 100 · confidence 60
3 matched · 0 unknown
EgoStation GoPro Pick-and-Place
Pick-and-place benchmark fixtures
provisional fit 100 · confidence 60
3 matched · 0 unknown
More evidenceShowHide
Additional decision dimensions
Access and license are declared by the source.
75 · c85Required signal categories are structured; exact tensor and action interfaces still need verification.
76 · c70Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.
70 · c55Artifact facts
- release_year
- official_claim
- 2024
- curated official source year
- model.required_signals
- official_claim
- observation · actions · robot state · language intent · goal image
- Official model documentation and curated signal mapping
- model.hub
- official_claim
- declared
- curated model hub link
- model.config_files
- metadata_verified
- repository: rail-berkeley/octo-small-1.5 · count: 1 · sample files: config.json
- Hugging Face model metadata siblings
- license.weights
- metadata_verified
- mit
- Hugging Face model card license metadata
- license
- metadata_verified
- mit
- Hugging Face model card license metadata
- release_timing
- metadata_verified
- 2024-05-20T17:57:01.000Z
- arXiv 2405.12213 published
- discovery.source
- secondary_claim
- name: worldbench/awesome-embodied-data-pyramid · trust: discovery_only · revision: 0568599d0619f20946e8089f6a941d0d9e30b690
- #table-embodied-foundation-models-vla-wam, table 17, row 3
- discovery.raw_fields
- secondary_claim
- Time: 2024.5 · Method: Octo · Institution: UC Berkeley · Project: [](https://octo-models.github.io/) · Model: VLA · Data: ![Real][data-real]
- #table-embodied-foundation-models-vla-wam, table 17, row 3
- model.reported_release_time
- secondary_claim
- 2024.5
- #table-embodied-foundation-models-vla-wam, table 17, row 3
- model.type
- secondary_claim
- VLA
- #table-embodied-foundation-models-vla-wam, table 17, row 3
- model.training_data_layers
- secondary_claim
- real_robot
- #table-embodied-foundation-models-vla-wam, table 17, row 3
- model.institution
- secondary_claim
- UC Berkeley
- #table-embodied-foundation-models-vla-wam, table 17, row 3
- 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 3
Additional references
OpenBot notes
- Good bridge between pure policy learning and broader VLA/WAM framing.
- OpenBot can help decide whether a dataset has enough action/state structure for Octo-style fine-tuning.