OpenBot
Platform
PolicyResearch

RoboCat

Google DeepMind

Self-improving generalist robotic agent that collects new demonstrations to improve its own manipulation capabilities.

Code
Not listed
Weights
Not listed
Checkpoint
Not listed
License
Unverified
PaperModel index A–Z

Decision summary

Best for

Self-improvement loop quality

Main blocker

Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.

Next check

Verify repository and artifact licenses.

Release history

Source-backed release timing for this canonical model record.

Important releases

  1. Model series introduced

    Release timing is anchored to the cited paper publication date.

    Paper

Evidence profile

A visual read of adoption evidence. Scores describe catalog evidence readiness, not task performance.

Published evaluation

1/6

dimensions scored

Unknown evidence remains visible and is never treated as a zero.

Access and governanceNot scoredconf. 20
Artifact availabilityNot scoredconf. 15
Training and loading reproducibilityNot scoredconf. 15
Training data requirements76conf. 70
Evaluation evidenceNot scoredconf. 10
Deployment readinessNot scoredconf. 10

Why these scores

The strongest decision reasons behind the evidence profile.

Top 3 adoption signals

Artifact availability

Unknown

Code and weights are not verified.

— · confidence 15

Training and loading reproducibility

Unknown

No verified loading configuration is available.

— · confidence 15

Evaluation evidence

Unknown

No structured evaluation evidence has been verified.

— · confidence 10

Data requirements

Declared loop-data needs, missing evidence, and the next checks that matter.

4 required categories

Observation / ego video

observation

required

Action / robot state

actions · robot state

required

Feedback / correction / failure

task success · feedback/failure

required

Sim-real / embodiment metadata

robot state

required

Critical gaps

Core categories are represented. Interface alignment and data quality still require verification.

Linked datasets

Signal-level links only. Verify runtime interfaces before use.

Browse datasets

EgoWorld

Schema validation for LeRobot v3

Useful

provisional fit 83 · confidence 40

2 matched · 1 unknown

MicroAGI01

Household skill segmentation

Useful

provisional fit 83 · confidence 40

2 matched · 1 unknown

EgoStation GoPro Pick-and-Place

Pick-and-place benchmark fixtures

Useful

provisional fit 83 · confidence 40

2 matched · 1 unknown

More evidenceShow

Additional decision dimensions

Access and governanceUnknown

License or access terms are not verified.

— · c20
Training data requirementsUseful

Required signal categories are structured; exact tensor and action interfaces still need verification.

76 · c70
Deployment readinessUnknown

Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.

— · c10

Artifact facts

release_year
official_claim
2023
curated official source year
model.required_signals
official_claim
observation · actions · robot state · task success · feedback/failure
Official model documentation and curated signal mapping
release_timing
metadata_verified
2023-06-20T17:35:20.000Z
arXiv 2306.11706 published

Additional references

DeepMind postPost

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.