MolmoAct Dataset
Ai2's self-collected MolmoAct real-robot manipulation dataset, used to train MolmoAct action reasoning models, covering household and tabletop domains.
Source notes
Ai2's self-collected MolmoAct real-robot manipulation dataset, used to train MolmoAct action reasoning models, covering household and tabletop domains. The Household split has 7,529 episodes across 115 tasks with varying viewpoints; the Tabletop split adds 2,959 episodes across 19 tasks with a fixed exocentric camera (~10,488 episodes total). It uses a Franka arm with three RGB views (primary, secondary, wrist) and a 7-dim end-effector action space, in LeRobot format with per-episode language annotations.
- Scale
- 22 hours
- Formats
- lerobot
- License
- Apache-2.0
- Published
- 2026-05-04
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
- Allen Institute for AI (Ai2)
- Evidence
- secondary claim
- Formats
- lerobot
- episodes
- 10.5K
- hours
- 22
- tasks
- 134
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 · Ai2's self-collected MolmoAct real-robot manipulation dataset, used to train MolmoAct action reasoning models, covering household and tabletop domains. The Household split has 7,529 episodes across 115 tasks with varying viewpoints; the Tabletop split adds 2,959 episodes across 19 tasks with a fixed exocentric camera (~10,488 episodes total). It uses a Franka arm with three RGB views (primary, secondary, wrist) and a 7-dim end-effector action space, in LeRobot format with per-episode language annotations.
presentee_pose · Ai2's self-collected MolmoAct real-robot manipulation dataset, used to train MolmoAct action reasoning models, covering household and tabletop domains. The Household split has 7,529 episodes across 115 tasks with varying viewpoints; the Tabletop split adds 2,959 episodes across 19 tasks with a fixed exocentric camera (~10,488 episodes total). It uses a Franka arm with three RGB views (primary, secondary, wrist) and a 7-dim end-effector action space, in LeRobot format with per-episode language annotations.
partiallanguage · Ai2's self-collected MolmoAct real-robot manipulation dataset, used to train MolmoAct action reasoning models, covering household and tabletop domains. The Household split has 7,529 episodes across 115 tasks with varying viewpoints; the Tabletop split adds 2,959 episodes across 19 tasks with a fixed exocentric camera (~10,488 episodes total). It uses a Franka arm with three RGB views (primary, secondary, wrist) and a 7-dim end-effector action space, in LeRobot format with per-episode language annotations.
presentNo decision-grade evidence captured yet.
unknownOpen · Apache-2.0 · lerobot
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.