Records
11
HF AUTHOR CLUSTER
oxe-auge robotics datasets
11 robotics-tagged HF records from oxe-auge, totaling 4,081 cumulative downloads. Some records cite published arxiv research.
DIRECT ANSWER
Author clusters consolidate every record from one publisher into a single buyer-review surface. oxe-auge ships 11 robotics datasets on Hugging Face. Top license: cc-by-4.0. Tier breakdown: 11 indexable as Tier A, 0 as Tier B, 0 demoted (those URLs redirect here).
Cumulative downloads
4,081
Top license
cc-by-4.0
DATASETS
All 11 robotics records from oxe-auge
11 of 11 datasets
bridge_train_0_5000_augmented
555 downloads · cc-by-4.0
bridge_train_0_5000_augmented Overview Codebase version: v3.0 Robots: google_robot, images, jaco, kinova3, kuka_iiwa, panda, sawyer, ur5e, xarm7 FPS: 5 Episodes: 5,000 Frames: 170,417 Splits: train: 0
bridge_train_20000_25460_augmented
404 downloads · cc-by-4.0
bridge_train_20000_25460_augmented Overview Codebase version: v3.0 Robots: google_robot, images, jaco, kinova3, kuka_iiwa, panda, sawyer, ur5e, xarm7 FPS: 5 Episodes: 5,460 Frames: 185,428 Splits: tra
berkeley_autolab_ur5_train_400_500_augmented
369 downloads · cc-by-4.0
berkeley_autolab_ur5_train_400_500 Overview Codebase version: v2.1 Robots: google_robot, images, jaco, kinova3, kuka_iiwa, panda, sawyer, widowX, xarm7 FPS: 5.0 Episodes: 100 Frames: 9,479 Videos: 900
iamlab_cmu_pickup_insert_train_500_631_augmented
357 downloads · cc-by-4.0
iamlab_cmu_pickup_insert_train_500_631_augmented Overview Codebase version: v2.1 Robots: google_robot, images, jaco, kinova3, kuka_iiwa, sawyer, ur5e, widowX, xarm7 FPS: 20.0 Episodes: 131 Frames: 30,
berkeley_autolab_ur5_train_300_400_augmented
355 downloads · cc-by-4.0
berkeley_autolab_ur5_train_300_400 Overview Codebase version: v2.1 Robots: google_robot, images, jaco, kinova3, kuka_iiwa, panda, sawyer, widowX, xarm7 FPS: 5.0 Episodes: 100 Frames: 9,934 Videos: 900
bridge_test_0_3475_augmented
354 downloads · cc-by-4.0
bridge_test_0_3475_augmented Overview Codebase version: v3.0 Robots: google_robot, images, jaco, kinova3, kuka_iiwa, panda, sawyer, ur5e, xarm7 FPS: 5 Episodes: 3,475 Frames: 118,603 Splits: train: 0:
bridge_train_15000_20000_augmented
351 downloads · cc-by-4.0
bridge_train_15000_20000_augmented Overview Codebase version: v3.0 Robots: google_robot, images, jaco, kinova3, kuka_iiwa, panda, sawyer, ur5e, xarm7 FPS: 5 Episodes: 5,000 Frames: 169,127 Splits: tra
bridge_train_5000_10000_augmented
350 downloads · cc-by-4.0
bridge_train_5000_10000_augmented Overview Codebase version: v3.0 Robots: google_robot, images, jaco, kinova3, kuka_iiwa, panda, sawyer, ur5e, xarm7 FPS: 5 Episodes: 5,000 Frames: 168,737 Splits: trai
bridge_train_10000_15000_augmented
335 downloads · cc-by-4.0
bridge_train_10000_15000_augmented Overview Codebase version: v3.0 Robots: google_robot, images, jaco, kinova3, kuka_iiwa, panda, sawyer, ur5e, xarm7 FPS: 5 Episodes: 5,000 Frames: 170,583 Splits: tra
berkeley_autolab_ur5_train_800_896_augmented
330 downloads · cc-by-4.0
berkeley_autolab_ur5_train_800_896 Overview Codebase version: v2.1 Robots: google_robot, images, jaco, kinova3, kuka_iiwa, panda, sawyer, widowX, xarm7 FPS: 5.0 Episodes: 96 Frames: 9,360 Videos: 864
berkeley_autolab_ur5_train_0_100_augmented
321 downloads · cc-by-4.0
berkeley_autolab_ur5_train_0_100 Overview Codebase version: v2.1 Robots: google_robot, images, jaco, kinova3, kuka_iiwa, panda, sawyer, widowX, xarm7 FPS: 5.0 Episodes: 100 Frames: 9,863 Videos: 900 C
RESEARCH PATHS
Use this record as part of a broader dataset review
A dataset record is only useful when it connects into the rest of the buyer workflow. The next review step is usually not another summary; it is a fit check, rights triage, source comparison, or custom bounty spec that names the missing proof.
For physical AI teams, the hard question is whether the public source can support a specific model objective under real deployment constraints. That requires adjacent dataset records, tools, comparisons, and sourcing paths, plus external references that a reviewer can open and challenge.
Use the links below to keep the review grounded. Start broad when discovery is incomplete, move into profile and comparison pages when the candidate source is known, and switch to custom collection when the blocker is rights, consent, geography, robot embodiment, or target environment coverage.
INTERNAL LINKS
Continue the buyer workflow
Physical AI dataset catalog
Use the catalog to compare source-backed dataset profiles by modality, task, rights signal, consent risk, and deployment fit.
Hugging Face robotics index
Scan the broader robotics dataset surface before narrowing into promoted profiles, comparisons, and custom collection specs.
Dataset changelog
Track source updates, licensing notes, and buyer-readiness changes that should trigger a renewed review.
Dataset fit checker
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License risk checker
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Data spec generator
Turn a public-source gap into a scoped capture request with sample QA, metadata, and delivery requirements.
Vendor alternatives hub
Compare data providers when the answer is not another public dataset but a better sourcing or capture route.
Data annotation companies
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EXTERNAL REFERENCES
Source context to verify
Scale AI physical AI data engine
Market context for why physical AI systems need custom, enriched, real-world data beyond generic labeling workflows.
LeRobot documentation
Robotics dataset and tooling context for Hugging Face based collection, sharing, conversion, and training workflows.
Open X-Embodiment
A cross-embodiment robotics dataset reference for comparing trajectory scale, robot diversity, and VLA training assumptions.
DROID dataset
A large in-the-wild robot manipulation dataset reference for real-world trajectory capture and deployment transfer risk.
TRUELABEL ROUTING
Need data like oxe-auge ships, but with cleaner rights?
If the Hub records don't carry the license, consent, or deployment fit your team needs, commission a custom collection on the same modality with explicit commercial terms.