OpenBot
Platform
Egocentric datasetOpen
OBRS 37•Bronze

EgoDex

EgoDex is a large-scale egocentric human manipulation dataset from Apple, comprising 829 hours of 1080p 30Hz video (90M frames) recorded with Apple Vision Pro.

Source notes

EgoDex is a large-scale egocentric human manipulation dataset from Apple, comprising 829 hours of 1080p 30Hz video (~90M frames) recorded with Apple Vision Pro. It pairs each frame with 3D pose annotations for the head, upper body, and hands (68 joints) via on-device tracking, plus camera intrinsics and natural-language task descriptions. The data spans 194 diverse tabletop tasks with everyday objects (~338K episodes), intended for imitation learning of dexterous manipulation from human video.

Scale
829 hours
Formats
hdf5
License
CC-BY-NC-ND-4.0
Published
2025-05-16
Official sourceAll recordsDataset index A–ZRun local preflightRequest scoped audit

Decision summary

Best for

human_demo

Main blocker

Metadata requires review against the official source before publication.

Next check

Read the dataset manifest and feature schema.

Release history

Source-backed release timing for this canonical dataset record.

Important releases

  1. Dataset series published

    Release timing is recorded from official dataset metadata.

    Release evidence

Selection readiness

6 evidence dimensions for deciding whether this dataset is ready to inspect, compare, or adopt. This is not a model benchmark.

Catalog evidence · not task performance

73/100

Provisional

4/6 dimensions scored · 48% confidence

Access and governance75conf. 85
Schema and signal coverageNot scoredconf. 15
Policy training readiness85conf. 55
World-model readiness68conf. 50
Failure and recovery readinessNot scoredconf. 15
Download and processing readiness65conf. 65
Read the evidence behind all 6 dimensions

Access and governance

Access and license are declared by the source.

usefulfit 75 · confidence 85

Schema and signal coverage

No machine-readable schema has been verified yet.

unknownnot scored · confidence 15

Policy training readiness

Observation and action/state signals are declared; alignment quality still depends on sample verification.

usefulfit 85 · confidence 55

World-model readiness

Temporal observations plus geometry or semantic context are declared; sample alignment remains to be audited.

usefulfit 68 · confidence 50

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

  • Gaze / attention. Not enough evidence is available to classify this signal.

Next checks

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

Dataset facts

Source
Apple
Evidence
secondary claim
Formats
hdf5
episodes
338K
hours
829
tasks
194
Read paper

Loop signals

6/7 present or partial

Gaze / attention

No decision-grade evidence captured yet.

unknown
View 6 more signal categories
Observation / ego video

video · EgoDex is a large-scale egocentric human manipulation dataset from Apple, comprising 829 hours of 1080p 30Hz video (~90M frames) recorded with Apple Vision Pro. It pairs each frame with 3D pose annotations for the head, upper body, and hands (68 joints) via on-device tracking, plus camera intrinsics and natural-language task descriptions. The data spans 194 diverse tabletop tasks with everyday objects (~338K episodes), intended for imitation learning of dexterous manipulation from human video.

present
Action / hand pose / robot state

ee_pose · EgoDex is a large-scale egocentric human manipulation dataset from Apple, comprising 829 hours of 1080p 30Hz video (~90M frames) recorded with Apple Vision Pro. It pairs each frame with 3D pose annotations for the head, upper body, and hands (68 joints) via on-device tracking, plus camera intrinsics and natural-language task descriptions. The data spans 194 diverse tabletop tasks with everyday objects (~338K episodes), intended for imitation learning of dexterous manipulation from human video.

partial
Language intent / task phase

language · EgoDex is a large-scale egocentric human manipulation dataset from Apple, comprising 829 hours of 1080p 30Hz video (~90M frames) recorded with Apple Vision Pro. It pairs each frame with 3D pose annotations for the head, upper body, and hands (68 joints) via on-device tracking, plus camera intrinsics and natural-language task descriptions. The data spans 194 diverse tabletop tasks with everyday objects (~338K episodes), intended for imitation learning of dexterous manipulation from human video.

present
Feedback / correction / failure

EgoDex is a large-scale egocentric human manipulation dataset from Apple, comprising 829 hours of 1080p 30Hz video (~90M frames) recorded with Apple Vision Pro. It pairs each frame with 3D pose annotations for the head, upper body, and hands (68 joints) via on-device tracking, plus camera intrinsics and natural-language task descriptions. The data spans 194 diverse tabletop tasks with everyday objects (~338K episodes), intended for imitation learning of dexterous manipulation from human video.

partial
Sim-real pairing

EgoDex is a large-scale egocentric human manipulation dataset from Apple, comprising 829 hours of 1080p 30Hz video (~90M frames) recorded with Apple Vision Pro. It pairs each frame with 3D pose annotations for the head, upper body, and hands (68 joints) via on-device tracking, plus camera intrinsics and natural-language task descriptions. The data spans 194 diverse tabletop tasks with everyday objects (~338K episodes), intended for imitation learning of dexterous manipulation from human video.

present
License / format / access

Open · CC-BY-NC-ND-4.0 · hdf5

partial
Evidence details and provenance

Official signal claims

Ee_poseLanguageVideo

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

  • Metadata requires review against the official source before publication.