Mobile ALOHA
Mobile ALOHA is a teleoperated mobile bimanual manipulation dataset collected by Fu, Zhao, and Finn (Stanford) using the low-cost whole-body Mobile ALOHA system, with 50 human demonstrations per task across whole-body…
Source notes
Mobile ALOHA is a teleoperated mobile bimanual manipulation dataset collected by Fu, Zhao, and Finn (Stanford) using the low-cost whole-body Mobile ALOHA system, with ~50 human demonstrations per task across whole-body tasks such as sauteing shrimp, opening a two-door cabinet, calling/entering an elevator, and rinsing a pan. The TFDS/Open X release contains 276 episodes with 3 RGB cameras (overhead + two wrist cameras at 480x640), a 14-dim state, a 16-dim action, and per-step language instructions. Raw HDF5 demonstrations are also distributed via the project's Google Drive.
- Scale
- 50.9 GB size
- Formats
- hdf5 · rlds
- License
- CC-BY-4.0
- Published
- 2024-01-04
- Embodiments
- aloha
- Modalities
- language, proprioception, rgb
Decision summary
teleoperation
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
How scores work73/100
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.
- Sim-real pairing. 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.
Engineering checks (OBRS) behind this record
Needs Audit
OBRS metadata and reported test evidence. A breakdown behind Selection readiness, not a separate score or an independent OpenBot certification.
- A passing real-hardware test report is required.
- A passing ingestion/pipeline test report is required.
- A passing privacy and provenance review is required.
Dataset facts
- Source
- Stanford University
- Evidence
- secondary claim
- Formats
- hdf5 · rlds
- episodes
- 276
- size
- 50.9 GB
- Source-reported scale
- 276 trajectories
Loop signals
4/7 present or partial
No decision-grade evidence captured yet.
unknownNo decision-grade evidence captured yet.
unknownView 5 more signal categories
video · Mobile ALOHA is a teleoperated mobile bimanual manipulation dataset collected by Fu, Zhao, and Finn (Stanford) using the low-cost whole-body Mobile ALOHA system, with ~50 human demonstrations per task across whole-body tasks such as sauteing shrimp, opening a two-door cabinet, calling/entering an elevator, and rinsing a pan. The TFDS/Open X release contains 276 episodes with 3 RGB cameras (overhead + two wrist cameras at 480x640), a 14-dim state, a 16-dim action, and per-step language instructions. Raw HDF5 demonstrations are also distributed via the project's Google Drive.
presentMobile ALOHA is a teleoperated mobile bimanual manipulation dataset collected by Fu, Zhao, and Finn (Stanford) using the low-cost whole-body Mobile ALOHA system, with ~50 human demonstrations per task across whole-body tasks such as sauteing shrimp, opening a two-door cabinet, calling/entering an elevator, and rinsing a pan. The TFDS/Open X release contains 276 episodes with 3 RGB cameras (overhead + two wrist cameras at 480x640), a 14-dim state, a 16-dim action, and per-step language instructions. Raw HDF5 demonstrations are also distributed via the project's Google Drive.
partiallanguage · Mobile ALOHA is a teleoperated mobile bimanual manipulation dataset collected by Fu, Zhao, and Finn (Stanford) using the low-cost whole-body Mobile ALOHA system, with ~50 human demonstrations per task across whole-body tasks such as sauteing shrimp, opening a two-door cabinet, calling/entering an elevator, and rinsing a pan. The TFDS/Open X release contains 276 episodes with 3 RGB cameras (overhead + two wrist cameras at 480x640), a 14-dim state, a 16-dim action, and per-step language instructions. Raw HDF5 demonstrations are also distributed via the project's Google Drive.
presentNo decision-grade evidence captured yet.
unknownOpen · CC-BY-4.0 · hdf5 · 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.