RoboCat
Google DeepMind
Self-improving generalist robotic agent that collects new demonstrations to improve its own manipulation capabilities.
Self-improvement loop quality
A useful reference for OpenBot's collect-evaluate-improve loop, even without public weights.
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
Language intent / task phase
task success · Task success and failure feedback to decide what to collect next · Cross-task and cross-robot traces that preserve adaptation history · Few-shot adaptation to new tasks
Action / robot state
actions · robot state
Future state / dynamics
Needs future-state supervision or rollout structure to validate predictive dynamics.
Feedback / correction / failure
task success · feedback/failure · Task success and failure feedback to decide what to collect next · Failure-driven data collection
Sim-real / embodiment metadata
robot state · Robot demonstrations connected to self-generated data collection · Cross-task and cross-robot traces that preserve adaptation history
Evaluation focus
- Self-improvement loop quality
- Few-shot adaptation to new tasks
- Failure-driven data collection
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 reference for OpenBot's collect-evaluate-improve loop, even without public weights.
- Turns failure mining from a report into a data acquisition strategy.