Ego-Exo4D Dataset
Learning from skilled human demonstrations
A multimodal skilled-activity dataset with time-synced egocentric and exocentric cameras plus language, pose, masks, and proficiency annotations.
Learning from skilled human demonstrations
Especially relevant when a task needs both wearable camera context and external validation views.
Inspect schema and run a bounded sample audit before committing to the full release.
Observation and action/state signals are declared; alignment quality still depends on sample verification.
fit 70 · confidence 55
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
video · external camera · Especially relevant when a task needs both wearable camera context and external validation views. · A multimodal skilled-activity dataset with time-synced egocentric and exocentric cameras plus language, pose, masks, and proficiency annotations.
Action / hand pose / robot state
pose · A multimodal skilled-activity dataset with time-synced egocentric and exocentric cameras plus language, pose, masks, and proficiency annotations.
Gaze / attention
No decision-grade evidence captured yet.
Language intent / task phase
language · JSON annotations · Converting human keysteps into robot task graphs · Especially relevant when a task needs both wearable camera context and external validation views.
Feedback / correction / failure
Cross-view reconstruction and evaluation · A multimodal skilled-activity dataset with time-synced egocentric and exocentric cameras plus language, pose, masks, and proficiency annotations.
Sim-real pairing
Cross-view reconstruction and evaluation
License / format / access
License required · Ego-Exo4D License Agreement · Ego-Exo4D 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 and action/state signals are declared; alignment quality still depends on sample verification.
fit 70 · confidence 55
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.
- Read the dataset manifest and feature schema.
- Run a bounded sample audit before assigning Strong readiness.
Raw dataset signals
OpenBot fit
- Learning from skilled human demonstrations
- Cross-view reconstruction and evaluation
- Converting human keysteps into robot task graphs
Related models and papers
Model references linked to similar loop signals.
lingbot-vision
Self-supervised learning for spatial perception
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.
lingbot-depth
Masked Depth Modeling for Spatial Perception
Gemini Robotics
Embodied reasoning and action model family that brings Gemini capabilities into robotics through vision, language, and physical action.
Integration notes
- Especially relevant when a task needs both wearable camera context and external validation views.
- The expert commentary and keysteps are useful for Data curation labels.