SmolVLA
Hugging Face LeRobot
Compact open vision-language-action policy designed for practical robot fine-tuning and deployment through the LeRobot ecosystem.
Data-efficient fine-tuning
Strong fit for OpenBot's model-readiness view because public weights make the training path concrete.
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 · LeRobot-style trajectories with observations, actions, and task text
Language intent / task phase
language intent · task success · LeRobot-style trajectories with observations, actions, and task text · Failure cases caused by weak task labels or noisy action traces
Action / robot state
actions · robot state · LeRobot-style trajectories with observations, actions, and task text · Small but clean demonstrations that preserve episode boundaries and action timing
Future state / dynamics
Needs future-state supervision or rollout structure to validate predictive dynamics.
Feedback / correction / failure
task success · Evaluation splits that measure fine-tuning data efficiency · Failure cases caused by weak task labels or noisy action traces
Sim-real / embodiment metadata
robot state · LeRobot-style trajectories with observations, actions, and task text · Practical deployment on smaller robotics stacks
Evaluation focus
- Data-efficient fine-tuning
- Practical deployment on smaller robotics stacks
- Failure cases caused by weak task labels or noisy action traces
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
- 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.