Records
4
HF AUTHOR CLUSTER
HuggingFaceVLA robotics datasets
4 robotics-tagged HF records from HuggingFaceVLA, totaling 36,775 cumulative downloads. Some records cite published arxiv research.
DIRECT ANSWER
Author clusters consolidate every record from one publisher into a single buyer-review surface. HuggingFaceVLA ships 4 robotics datasets on Hugging Face. Top license: apache-2.0. Of those, 4 get a full standalone page, 0 get a shorter profile, and 0 are folded into this cluster.
Cumulative downloads
36,775
Top license
apache-2.0
DATASETS
All 4 robotics records from HuggingFaceVLA
4 of 4 datasets
libero
Published Sep 2025 · apache-2.0 · HuggingFaceVLA
This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "robot_type": "panda", "total_episodes": 1693, "total_frames": 273465, "total_tasks": 40, "chunks_size": 1000, "fps": 10.0, "splits": {…
- 24,673 downloads
- 51 likes
- 100K<n<1M
community_dataset_v3
Published Dec 2025 · apache-2.0 · HuggingFaceVLA
Lerobot Community Datasets v3 - A Cross-Embodiment Pretraining Dataset for Vision Language Action Models A large-scale robotics dataset for vision-language-action learning, featuring 791 datasets across 46 robot types, enabling…
- 4,529 downloads
- 22 likes
- 10M<n<100M
- Paper available
community_dataset_v1
Published Nov 2025 · apache-2.0 · HuggingFaceVLA
Community Dataset v1 A large-scale community-contributed robotics dataset for vision-language-action learning, featuring 128 datasets from 55 contributors worldwide. We used this dataset to pretrain SmolVLA.
- 4,357 downloads
- 5 likes
- 10K<n<100K
- Paper available
community_dataset_v2
Published Nov 2025 · apache-2.0 · HuggingFaceVLA
Community Dataset v2 A large-scale community-contributed robotics dataset for vision-language-action learning, featuring 340 datasets from 117 contributors worldwide.
- 3,216 downloads
- 3 likes
- n<1K
- 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 HuggingFaceVLA 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.