Records
5
HF AUTHOR CLUSTER
BeingBeyond robotics datasets
5 robotics-tagged HF records from BeingBeyond, totaling 2,975 cumulative downloads. Some records cite published arxiv research.
DIRECT ANSWER
Author clusters consolidate every record from one publisher into a single buyer-review surface. BeingBeyond ships 5 robotics datasets on Hugging Face. Top license: cc-by-4.0. Of those, 5 get a full standalone page, 0 get a shorter profile, and 0 are folded into this cluster.
Cumulative downloads
2,975
Top license
cc-by-4.0
DATASETS
All 5 robotics records from BeingBeyond
5 of 5 datasets
PND_Adam-U_pick-simple
Published Jan 2026 · cc-by-4.0 · BeingBeyond
This data is part of the training data for Being-H0.5, produced by BeingBeyond. License This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
- 1,030 downloads
- 4 likes
- 1M<n<10M
- Paper available
PND_Adam-U_pick-simple_speed2x
Published Jan 2026 · cc-by-4.0 · BeingBeyond
This data is part of the training data for Being-H0.5, produced by BeingBeyond. License This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
- 650 downloads
- 1 likes
- 100K<n<1M
- Paper available
UniHand_Preview
Published Apr 2026 · not specified · BeingBeyond
This data is a subset of the pretraining data for Being-H0.5. Citation Being-H0.5 @article{beingbeyond2026beingh05, title={Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization}, author={Luo, Hao and Wang, Ye…
- 621 downloads
- 5 likes
- 10K<n<100K
- Paper available
h0_post_train_db_2508
Published Aug 2025 · mit · BeingBeyond
Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos We introduce Being-H0, the first dexterous Vision-Language-Action model pretrained from large-scale human videos via explicit hand motion modeling.
- 357 downloads
- 1 likes
- 10K<n<100K
- Paper available
PND_Adam-U_pour-cup
Published Jan 2026 · cc-by-4.0 · BeingBeyond
This data is part of the training data for Being-H0.5, produced by BeingBeyond. License This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
- 317 downloads
- 10K<n<100K
- Paper available
KEEP DIGGING
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.
Where to go next
- Physical AI dataset catalogUse the catalog to compare source-backed dataset profiles by modality, task, rights signal, consent risk, and deployment fit.
- Hugging Face robotics indexScan the broader robotics dataset surface before narrowing into promoted profiles, comparisons, and custom collection specs.
- Dataset changelogTrack source updates, licensing notes, and buyer-readiness changes that should trigger a renewed review.
- Dataset fit checkerScore whether a public source is enough for the model, rights path, modalities, and target environment.
- License risk checkerSeparate source license language from contributor consent, redistribution, private-space risk, and model-use assumptions.
- Data spec generatorTurn a public-source gap into a scoped capture request with sample QA, metadata, and delivery requirements.
- Vendor alternatives hubCompare data providers when the answer is not another public dataset but a better sourcing or capture route.
- Data annotation companiesUse the company index to separate annotation vendors, data engines, marketplaces, and specialist capture teams.
Other places to verify the claims
- Scale AI physical AI data engineMarket context for why physical AI systems need custom, enriched, real-world data beyond generic labeling workflows.
- LeRobot documentationRobotics dataset and tooling context for Hugging Face based collection, sharing, conversion, and training workflows.
- Open X-EmbodimentA cross-embodiment robotics dataset reference for comparing trajectory scale, robot diversity, and VLA training assumptions.
- DROID datasetA large in-the-wild robot manipulation dataset reference for real-world trajectory capture and deployment transfer risk.
TRUELABEL ROUTING
Need data like BeingBeyond 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.