RT-1
Google Robotics
Robotics Transformer policy trained on large-scale real-world robot demonstrations for language-conditioned manipulation.
Real-world task success
A useful baseline for understanding why policy learning needs action/state alignment, not only video.
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
UnknownArtifact availability is unknown.
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 · Large-scale robot episodes with images, language commands, and actions
Language intent / task phase
language intent · task success · Large-scale robot episodes with images, language commands, and actions · Task diversity across objects, scenes, and long-horizon instructions
Action / robot state
actions · robot state · Large-scale robot episodes with images, language commands, and actions
Future state / dynamics
Task diversity across objects, scenes, and long-horizon instructions · Real-world task success
Feedback / correction / failure
task success · Robust train/test splits that expose distribution shift and recovery limits · Real-world task success · Failure modes from sparse recovery and missing feedback traces
Sim-real / embodiment metadata
robot state · Large-scale robot episodes with images, language commands, and actions
Evaluation focus
- Real-world task success
- Generalization to novel instructions and objects
- Failure modes from sparse recovery and missing feedback 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.
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 useful baseline for understanding why policy learning needs action/state alignment, not only video.
- Highlights the gap between egocentric observation corpora and robot-executable traces.