Records
4
HF AUTHOR CLUSTER
FedorX8 robotics datasets
4 robotics-tagged HF records from FedorX8, totaling 12,967 cumulative downloads. Some records cite published arxiv research.
DIRECT ANSWER
Author clusters consolidate every record from one publisher into a single buyer-review surface. FedorX8 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
12,967
Top license
apache-2.0
DATASETS
All 4 robotics records from FedorX8
4 of 4 datasets
humanoid-everyday
Published Mar 2026 · apache-2.0 · FedorX8
Humanoid Everyday A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation Overview Humanoid Everyday is a large-scale, diverse humanoid manipulation dataset designed for open-world robotic learning and embodied intelligence.
- 10,991 downloads
- 1K<n<10K
- Paper available
bridge_v2_lerobot
Published Feb 2026 · apache-2.0 · FedorX8
This dataset was created using LeRobot. Dataset Description Converted BridgeV2 dataset to lerobot format. Downloaded from [https://rail.eecs.berkeley.edu/datasets/bridge_release/data/] (only demos_8_17.zip, not scripted_6_18.zip) Dataset…
- 1,247 downloads
- 1M<n<10M
furniture_bench_dataset_lerobot
Published Feb 2026 · apache-2.0 · FedorX8
This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "robot_type": "franka", "total_episodes": 5100, "total_frames": 3948057, "total_tasks": 9, "chunks_size": 1000, "fps": 10, "splits": {…
- 411 downloads
- 1M<n<10M
language_table_lerobot
Published Feb 2026 · apache-2.0 · FedorX8
This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "robot_type": "xarm", "total_episodes": 442226, "total_frames": 7045476, "total_tasks": 127605, "chunks_size": 1000, "fps": 10,…
- 318 downloads
- 1M<n<10M
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 FedorX8 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.