RoboNet
RoboNet is a large-scale, open dataset for multi-robot learning, containing over 15 million video frames of robot-object interaction collected autonomously across four research labs (UC Berkeley BAIR, Stanford AI Lab,…
Source notes
RoboNet is a large-scale, open dataset for multi-robot learning, containing over 15 million video frames of robot-object interaction collected autonomously across four research labs (UC Berkeley BAIR, Stanford AI Lab, UPenn GRASP, Google Brain Robotics). It aggregates data from 7 robot platforms (Sawyer, Franka Panda, Baxter, Fetch, Google R3, Kuka LBR iiwa, WidowX) recorded from 113 unique camera viewpoints in tabletop settings, with each datapoint storing the camera RGB image, arm pose, force-sensor readings, gripper state and actions. The TFDS release exposes 162,417 trajectories with 5-dimensional action and state vectors.
- Scale
- 38.9 GB size
- Formats
- hdf5 · rlds
- License
- CC-BY-4.0
- Published
- 2019-10-24
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
70/100
Provisional
3/6 dimensions scored · 42% 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
World-model observation, geometry, or temporal semantics are not verified.
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.
- Language intent / task phase. 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.
Dataset facts
- Source
- UC Berkeley (BAIR), Stanford, UPenn GRASP, Google Brain
- Evidence
- secondary claim
- Formats
- hdf5 · rlds
- episodes
- 162.4K
- size
- 38.9 GB
- Source-reported scale
- 162K trajectories
Loop signals
3/7 present or partial
No decision-grade evidence captured yet.
unknownNo decision-grade evidence captured yet.
unknownView 5 more signal categories
video · RoboNet is a large-scale, open dataset for multi-robot learning, containing over 15 million video frames of robot-object interaction collected autonomously across four research labs (UC Berkeley BAIR, Stanford AI Lab, UPenn GRASP, Google Brain Robotics). It aggregates data from 7 robot platforms (Sawyer, Franka Panda, Baxter, Fetch, Google R3, Kuka LBR iiwa, WidowX) recorded from 113 unique camera viewpoints in tabletop settings, with each datapoint storing the camera RGB image, arm pose, force-sensor readings, gripper state and actions. The TFDS release exposes 162,417 trajectories with 5-dimensional action and state vectors.
presentee_pose · RoboNet is a large-scale, open dataset for multi-robot learning, containing over 15 million video frames of robot-object interaction collected autonomously across four research labs (UC Berkeley BAIR, Stanford AI Lab, UPenn GRASP, Google Brain Robotics). It aggregates data from 7 robot platforms (Sawyer, Franka Panda, Baxter, Fetch, Google R3, Kuka LBR iiwa, WidowX) recorded from 113 unique camera viewpoints in tabletop settings, with each datapoint storing the camera RGB image, arm pose, force-sensor readings, gripper state and actions. The TFDS release exposes 162,417 trajectories with 5-dimensional action and state vectors.
presentNo decision-grade evidence captured yet.
unknownNo 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.