Xperience-10M Dataset
World model pretraining
A large egocentric multimodal dataset with synchronized video streams, audio, depth, poses, mocap, IMU, and hierarchical language annotations.
World model pretraining
Very large and controlled-access; the practical OpenBot path is metadata indexing plus targeted subset pulls.
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 85 · confidence 55
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 · depth · camera pose · A large egocentric multimodal dataset with synchronized video streams, audio, depth, poses, mocap, IMU, and hierarchical language annotations.
Action / hand pose / robot state
camera pose · hand pose · A large egocentric multimodal dataset with synchronized video streams, audio, depth, poses, mocap, IMU, and hierarchical language annotations.
Gaze / attention
No decision-grade evidence captured yet.
Language intent / task phase
language · A large egocentric multimodal dataset with synchronized video streams, audio, depth, poses, mocap, IMU, and hierarchical language annotations.
Feedback / correction / failure
No decision-grade evidence captured yet.
Sim-real pairing
depth · camera pose · Real-to-sim and sim-to-real data alignment · A large egocentric multimodal dataset with synchronized video streams, audio, depth, poses, mocap, IMU, and hierarchical language annotations.
License / format / access
Gated · Apache-2.0 · Hugging Face dataset · multimodal episode files
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 85 · confidence 55
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
- Gaze / attentionunknownNot enough evidence to classify this signal. Verify metadata or a bounded sample.
- Feedback / correction / failureunknownNot 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
- World model pretraining
- Real-to-sim and sim-to-real data alignment
- Multimodal episode quality checks
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.
GR00T N1
Open foundation model for generalist humanoid robots, focused on whole-body and manipulation behavior from multimodal robot data.
NVIDIA Cosmos
World foundation model platform for physical AI, built around predictive world modeling and data processing workflows.
Genie 2
Large-scale foundation world model for generating action-controllable interactive environments from visual prompts.
UniSim
Interactive real-world simulator research that models how visual scenes change under actions and interaction.
Integration notes
- Very large and controlled-access; the practical OpenBot path is metadata indexing plus targeted subset pulls.
- Useful as a reference for the signals OpenBot Data should preserve.