Ego4D Dataset
Pretraining egocentric perception
A broad first-person video benchmark for daily activities, long-horizon understanding, narration, object interaction, and temporal memory.
Pretraining egocentric perception
Best treated as a large source corpus rather than a direct robot-action dataset.
Inspect schema and run a bounded sample audit before committing to the full release.
Observation/action alignment has not been established.
not scored · confidence 15
Temporal observations plus geometry or semantic context are declared; sample alignment remains to be audited.
fit 82 · confidence 50
No verified failure/recovery annotation evidence is available yet.
not scored · confidence 15
Verified facts and provenance
Claims, metadata verification, and sample verification are shown separately.
Unknown — no machine-readable schema facts have been captured.
Unknown — metadata conclusions do not prove sample coverage, alignment, or file integrity.
Declared loop signal coverage
Signals inferred from official metadata; Data pipeline verification is still pending.
Observation / ego video
video · stereo · A broad first-person video benchmark for daily activities, long-horizon understanding, narration, object interaction, and temporal memory.
Action / hand pose / robot state
Mining object interaction priors · Best treated as a large source corpus rather than a direct robot-action dataset. · Useful for building retrieval, narration, hand-object, and failure-mining tasks around OpenBot Data. · A broad first-person video benchmark for daily activities, long-horizon understanding, narration, object interaction, and temporal memory.
Gaze / attention
gaze
Language intent / task phase
narration · JSON annotations · Useful for building retrieval, narration, hand-object, and failure-mining tasks around OpenBot Data. · A broad first-person video benchmark for daily activities, long-horizon understanding, narration, object interaction, and temporal memory.
Feedback / correction / failure
Useful for building retrieval, narration, hand-object, and failure-mining tasks around OpenBot Data.
Sim-real pairing
3d scan
License / format / access
License required · Ego4D License Agreement · Ego4D CLI · MP4
Catalog decision scorecard
Access and license are declared by the source.
fit 75 · confidence 85
No machine-readable schema has been verified yet.
not scored · confidence 15
Observation/action alignment has not been established.
not scored · confidence 15
Temporal observations plus geometry or semantic context are declared; sample alignment remains to be audited.
fit 82 · confidence 50
No verified failure/recovery annotation evidence is available yet.
not scored · confidence 15
Scale is declared; transfer and processing estimates are not measured.
fit 65 · confidence 65
Good tasks
Blockers and unresolved evidence
- Read the dataset manifest and feature schema.
- Run a bounded sample audit before assigning Strong readiness.
Raw dataset signals
OpenBot fit
- Pretraining egocentric perception
- Long-horizon activity understanding
- Mining object interaction priors
Related models and papers
Model references linked to similar loop signals.
Looped World Models
World-model architecture using looped transformer refinement over latent states for iterative environment understanding.
lingbot-map
A feed-forward 3D foundation model for reconstructing scenes from streaming data
LingBot-World
An open interactive world simulator derived from video generation, with camera- and action-conditioned variants and long-horizon generation.
Gemini Robotics
Embodied reasoning and action model family that brings Gemini capabilities into robotics through vision, language, and physical action.
Integration notes
- Best treated as a large source corpus rather than a direct robot-action dataset.
- Useful for building retrieval, narration, hand-object, and failure-mining tasks around OpenBot Data.