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Data Scaling Laws

Data Scaling Laws in Imitation Learning for Robotic Manipulation

Scale
24098 trajectories Source-reported scale
Formats
Not declared
License
Not declared
Published
2025

Decision summary

Best for

Data Scaling Laws in Imitation Learning for Robotic Manipulation

Main blocker

No verified blocker is published; unresolved facts remain unknown.

Next check

Verify repository and artifact licenses.

Catalog assessment

Selection evidence

65/100

Provisional · confidence 24 · 1/6 evaluated

Access and governance

License or access terms are not verified.

unknownnot scored · confidence 20

Schema and signal coverage

No machine-readable schema has been verified yet.

unknownnot scored · confidence 15

Policy training readiness

Observation/action alignment has not been established.

unknownnot scored · confidence 15
View 3 more dimensions

World-model readiness

World-model observation, geometry, or temporal semantics are not verified.

unknownnot scored · confidence 15

Failure and recovery readiness

No verified failure/recovery annotation evidence is available yet.

unknownnot scored · confidence 15

Download and processing readiness

Scale is declared; transfer and processing estimates are not measured.

usefulfit 65 · confidence 65
Review unresolved evidence and next checks

Signal gaps

  • Observation / ego video. Not enough evidence is available to classify this signal.
  • Gaze / attention. Not enough evidence is available to classify this signal.
  • Language intent / task phase. Not enough evidence is available to classify this signal.
  • Feedback / correction / failure. Not enough evidence is available to classify this signal.
  • Sim-real pairing. Not enough evidence is available to classify this signal.

Next checks

  • Verify repository and artifact licenses.
  • Read the dataset manifest and feature schema.
  • Run a bounded sample audit before assigning Strong readiness.

Record specifics

Dataset facts

Source
Official source
Evidence
secondary claim
Formats
Not declared
Source-reported scale
24098 trajectories
Read paper

Metadata coverage

Loop signals

2/7 present or partial

Observation / ego video

No decision-grade evidence captured yet.

unknown
Gaze / attention

No decision-grade evidence captured yet.

unknown
Language intent / task phase

No decision-grade evidence captured yet.

unknown
View 4 more signal categories
Action / hand pose / robot state

Data Scaling Laws in Imitation Learning for Robotic Manipulation

partial
Feedback / correction / failure

No decision-grade evidence captured yet.

unknown
Sim-real pairing

No decision-grade evidence captured yet.

unknown
License / format / access

License required

partial
Evidence details and provenance

Official signal claims

Schema and annotations

No machine-readable schema facts are captured.

Sample verification

Pending. Metadata does not prove sample coverage, alignment, or file integrity.

curated official source

Integration notes

No integration notes are published.

Data Scaling Laws: Loop Signals, Model Fit, and Failure Mining Readiness · OpenBot.ai