OpenVLA
Stanford / UC Berkeley / Toyota Research Institute / collaborators
Open-source vision-language-action model for generalist robotic manipulation, trained on diverse real-world robot demonstrations.
Task success across objects and scenes
A strong anchor for mapping dataset readiness to VLA fine-tuning needs.
A model hub link is a declaration. Files, loadability, evaluation, and deployment are scored separately.
Model decision scorecard
Use-case scores and evidence confidence are separate; Unknown is not treated as failure.
Code, weights, and checkpoints
UsefulAn official model hub is linked; weight files and loadability are not verified.
Loading and training reproducibility
UnknownNo verified loading configuration is available.
Training data requirements
UsefulRequired signal categories are declared; exact tensor and action interfaces still need verification.
Evaluation evidence
UsefulEvaluation focus is declared, but metrics are not independently verified.
Deployment readiness
UnknownHardware, latency, dependencies, and runtime loading are not yet verified.
Artifact facts and provenance
No metadata-verified artifact facts yet. Source links remain declarations only.
Loop signal demand
Signals this model family needs for training, evaluation, or failure mining.
Observation / ego video
observation · Robot demonstrations with image observations and action traces
Language intent / task phase
language intent · task success · Language instructions or task labels aligned to episodes · Task success across objects and scenes
Action / robot state
actions · robot state · Robot demonstrations with image observations and action traces
Future state / dynamics
Task success across objects and scenes
Feedback / correction / failure
task success · Embodiment metadata for fine-tuning and evaluation splits · Task success across objects and scenes · Failure clusters where language grounding breaks
Sim-real / embodiment metadata
robot state · Robot demonstrations with image observations and action traces · Embodiment metadata for fine-tuning and evaluation splits
Evaluation focus
- Task success across objects and scenes
- Generalization after fine-tuning
- Failure clusters where language grounding breaks
Missing critical loop signals
Core signal demands are represented. Check quality, alignment, and access constraints.
Related catalog datasets
EgoWorld
Bimanual manipulation in LeRobot format
Dataset license restricts commercial use.
EgoStation GoPro Pick-and-Place
GoPro first-person pick-and-place trajectories
Dataset license restricts commercial use.
Egocentric Adjust Bottle
Apache-2.0 LeRobot bottle-adjustment task
Exact action dimensions, control frequency, normalization, and camera mapping require interface verification.
OpenBot notes
- A strong anchor for mapping dataset readiness to VLA fine-tuning needs.
- Pairs naturally with Catalog filters for action/state and intent/task phase signals.