EgoTracks Dataset
Object persistence in robot tasks
An Ego4D benchmark focused on tracking objects through heavy hand interaction, occlusion, viewpoint changes, and object disappearance/reappearance.
Object persistence in robot tasks
Useful for Bench/Data integration when failures involve losing an object through a manipulation step.
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
World-model observation, geometry, or temporal semantics are not verified.
not scored · confidence 15
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
Ego4D video
Action / hand pose / robot state
Occlusion-heavy hand-object tracking · Useful for Bench/Data integration when failures involve losing an object through a manipulation step. · An Ego4D benchmark focused on tracking objects through heavy hand interaction, occlusion, viewpoint changes, and object disappearance/reappearance.
Gaze / attention
No decision-grade evidence captured yet.
Language intent / task phase
JSON annotations · Object persistence in robot tasks
Feedback / correction / failure
Occlusion-heavy hand-object tracking · Failure replay around lost targets · Useful for Bench/Data integration when failures involve losing an object through a manipulation step. · An Ego4D benchmark focused on tracking objects through heavy hand interaction, occlusion, viewpoint changes, and object disappearance/reappearance.
Sim-real pairing
No decision-grade evidence captured yet.
License / format / access
License required · Ego4D License Agreement · JSON annotations · Ego4D video
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
World-model observation, geometry, or temporal semantics are not verified.
not scored · confidence 15
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
- Gaze / attentionunknownNot enough evidence to classify this signal. Verify metadata or a bounded sample.
- Sim-real pairingunknownNot enough evidence to classify this signal. Verify metadata or a bounded sample.
- Read the dataset manifest and feature schema.
- Run a bounded sample audit before assigning Strong readiness.
Raw dataset signals
OpenBot fit
- Object persistence in robot tasks
- Occlusion-heavy hand-object tracking
- Failure replay around lost targets
Related models and papers
Model references linked to similar loop signals.
Integration notes
- Useful for Bench/Data integration when failures involve losing an object through a manipulation step.
- Part of Ego4D, so access follows Ego4D terms.