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Google Research / Everyday Robots

Language-grounded robotics approach that combines language-model planning with affordance scores from robot skills.

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

Decision summary

Best for

Instruction decomposition

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

Language intent / task phase

language intent · task phase

required

Action / robot state

actions

required

Feedback / correction / failure

feedback/failure

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

EPIC-KITCHENS-100

human_demo

Useful

provisional fit 88 · confidence 45

3 matched · 1 unknown

Ego-Exo4D

human_demo

Useful

provisional fit 100 · confidence 60

4 matched · 0 unknown

EgoWorld

Schema validation for LeRobot v3

Useful

provisional fit 75 · confidence 30

2 matched · 2 unknown

More evidenceShow

Additional data signals

Sim-real / embodiment metadata

Descriptive model record

useful

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
2022
curated official source year
model.required_signals
official_claim
observation · language intent · task phase · feedback/failure · actions
Official model documentation and curated signal mapping
release_timing
metadata_verified
2022-04-04T17:57:11.000Z
arXiv 2204.01691 published

Additional references

ProjectProject

OpenBot notes

  • Important for tagging datasets with task phase and feedback, not just final action traces.
  • Useful bridge from language intent data to executable robot skills.