Open X-Embodiment (OXE)
Open X-Embodiment is a large-scale, standardized aggregation of robot learning datasets assembled by a collaboration of 21 institutions, pooling 60 existing datasets from 34 labs covering 22 robot embodiments and over 1…
Source notes
Open X-Embodiment is a large-scale, standardized aggregation of robot learning datasets assembled by a collaboration of 21 institutions, pooling 60 existing datasets from 34 labs covering 22 robot embodiments and over 1 million real robot trajectories demonstrating 527 skills (160,266 tasks). All datasets are converted to a common RLDS (Reinforcement Learning Datasets) episode format loadable via TensorFlow Datasets, and the corpus was used to train the cross-embodiment RT-1-X and RT-2-X generalist policies. It is widely used to train cross-embodiment models such as OpenVLA.
- Scale
- 1M episodes
- Formats
- rlds
- License
- CC-BY-4.0
- Published
- 2023-10-13
Decision summary
scripted
Metadata requires review against the official source before publication.
Read the dataset manifest and feature schema.
Release history
Source-backed release timing for this canonical dataset record.
- Release evidence
Dataset series published
Release timing is recorded from official dataset metadata.
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
77/100
Provisional
4/6 dimensions scored · 48% confidence
Read the evidence behind all 6 dimensions
Access and governance
Access and license are declared by the source.
Schema and signal coverage
No machine-readable schema has been verified yet.
Policy training readiness
Observation and action/state signals are declared; alignment quality still depends on sample verification.
World-model readiness
Temporal observations plus geometry or semantic context are declared; sample alignment remains to be audited.
Failure and recovery readiness
No verified failure/recovery annotation evidence is available yet.
Download and processing readiness
Scale is declared; transfer and processing estimates are not measured.
Review unresolved evidence and next checks
Signal gaps
- Gaze / attention. Not enough evidence is available to classify this signal.
- Feedback / correction / failure. 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
- Open X-Embodiment Collaboration (led by Google DeepMind)
- Evidence
- secondary claim
- Formats
- rlds
- episodes
- 1M
- tasks
- 160.3K
Loop signals
5/7 present or partial
No decision-grade evidence captured yet.
unknownNo decision-grade evidence captured yet.
unknownView 5 more signal categories
depth · video
presentee_pose
partiallanguage · Open X-Embodiment is a large-scale, standardized aggregation of robot learning datasets assembled by a collaboration of 21 institutions, pooling 60 existing datasets from 34 labs covering 22 robot embodiments and over 1 million real robot trajectories demonstrating 527 skills (160,266 tasks). All datasets are converted to a common RLDS (Reinforcement Learning Datasets) episode format loadable via TensorFlow Datasets, and the corpus was used to train the cross-embodiment RT-1-X and RT-2-X generalist policies. It is widely used to train cross-embodiment models such as OpenVLA.
presentdepth
presentOpen · CC-BY-4.0 · rlds
presentEvidence details and provenance
Official signal claims
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.