ACT
ALOHA / LeRobot ecosystem
Action Chunking with Transformers predicts short action sequences for efficient imitation learning in manipulation tasks.
Precision manipulation success
A practical baseline for dataset readiness because it needs clean action supervision.
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 · High-quality demonstrations with image and state observations
Language intent / task phase
task success · Task-specific splits that expose compounding-error failures · Data quality versus task completion
Action / robot state
actions · robot state · hand pose · Temporally aligned action chunks
Future state / dynamics
Needs future-state supervision or rollout structure to validate predictive dynamics.
Feedback / correction / failure
task success · Task-specific splits that expose compounding-error failures · Precision manipulation success · Compounding error and recovery behavior
Sim-real / embodiment metadata
robot state
Evaluation focus
- Precision manipulation success
- Compounding error and recovery behavior
- Data quality versus task completion
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.
MicroAGI01
Household manipulation with pose annotations
Exact action dimensions, control frequency, normalization, and camera mapping require interface verification.
EgoStation GoPro Pick-and-Place
GoPro first-person pick-and-place trajectories
Dataset license restricts commercial use.
OpenBot notes
- A practical baseline for dataset readiness because it needs clean action supervision.
- Useful when a dataset is too narrow for generalist policies but strong enough for task-specific imitation.