Records
13
HF AUTHOR CLUSTER
13 robotics-tagged HF records from nvidia, totaling 463,897 cumulative downloads. Some records cite published arxiv research.
DIRECT ANSWER
Author clusters consolidate every record from one publisher into a single buyer-review surface. nvidia ships 13 robotics datasets on Hugging Face. Top license: cc-by-4.0. Tier breakdown: 13 indexable as Tier A, 0 as Tier B, 0 demoted (those URLs redirect here).
13
463,897
cc-by-4.0
DATASETS
13 of 13 datasets
350,718 downloads · cc-by-4.0
PhysicalAI-Robotics-GR00T-X-Embodiment-Sim Github Repo: Isaac GR00T N1 We provide a set of datasets used for post-training of GR00T N1. Each dataset is a collection of trajectories from different robo
37,477 downloads · cc-by-4.0
Dataset Description: Open-H-Embodiment is a community‑driven dataset initiative building the open, shared foundation needed to train and evaluate AI autonomy models for surgical robotics and ultrasoun
30,417 downloads · cc-by-4.0
PhysicalAI-Robotics-Manipulation-Kitchen-Demos We provide a 600 hours of human-teleoperated demonstrations across 316 different tasks, totalling 55k trajectories. The datasets are collected using Fran
28,902 downloads · cc-by-4.0
PhysicalAI-Autonomous-Vehicle-Cosmos-Drive-Dreams Paper | Paper Website | GitHub Download We provide a download script to download our dataset. If you have enough space, you can use git to download a
5,705 downloads · cc-by-4.0
Dataset Description: PhysicalAI-Robotics-Manipulation-SingeArm is a collection of datasets of automatic generated motions of a Franka Panda robot performing operations such as block stacking, opening
4,673 downloads · cc-by-4.0
NVIDIA Physical AI SimReady Warehouse OpenUSD Dataset Dataset Version: 1.1.0 Date: May 18, 2025 Author: NVIDIA, Corporation License: CC-BY-4.0 (Creative Commons Attribution 4.0 International) Contents
1,477 downloads · cc-by-4.0
PhysicalAI Robotics Manipulation in the Kitchen Dataset Description: PhysicalAI-Robotics-Manipulation-Kitchen is a dataset of automatic generated motions of robots performing operations such as openin
1,096 downloads · cc-by-nc-4.0
Website | Model | Dataset | Code | Paper NitroGen Dataset Dataset Description: The NitroGen dataset contains action annotations for publicly available gameplay videos. Specifically, we used an in-hous
1,056 downloads · cc-by-4.0
GraspGen: Scaling Sim2Real Grasping GraspGen is a large-scale simulated grasp dataset for multiple robot embodiments and grippers. We release over 57 million grasps, computed for a subset of 8515 obje
751 downloads · cc-by-4.0
Dataset Description The Physical AI NuRec dataset seeks to empower robotic researchers to build the next generation of physical AI based end-to-end robotic models. This dataset includes various 3DGUT
668 downloads · not specified
PhysicalAI-Robotics-Manipulation-Objects is a dataset of automatic generated motions of robots performing operations such as picking and placing objects in a kitchen environment. The dataset was gener
622 downloads · cc-by-4.0
Dataset Description: The Arena-G1-Loco-Manipulation-Task dataset is multimodal collections of trajectories generated in Isaac Lab. It supports humanoid (G1) loco-manipulation task in IsaacLab-Arena en
335 downloads · cc-by-4.0
Dataset Description: This dataset is multimodal collections of trajectories generated in Isaac Lab. It supports humanoid (GR1) tabletop manipulation tasks for industrial settings. Each dataset entry p
RESEARCH PATHS
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
Use the catalog to compare source-backed dataset profiles by modality, task, rights signal, consent risk, and deployment fit.
Scan the broader robotics dataset surface before narrowing into promoted profiles, comparisons, and custom collection specs.
Track source updates, licensing notes, and buyer-readiness changes that should trigger a renewed review.
Score whether a public source is enough for the model, rights path, modalities, and target environment.
Separate source license language from contributor consent, redistribution, private-space risk, and model-use assumptions.
Turn a public-source gap into a scoped capture request with sample QA, metadata, and delivery requirements.
Compare data providers when the answer is not another public dataset but a better sourcing or capture route.
Use the company index to separate annotation vendors, data engines, marketplaces, and specialist capture teams.
EXTERNAL REFERENCES
Market context for why physical AI systems need custom, enriched, real-world data beyond generic labeling workflows.
Robotics dataset and tooling context for Hugging Face based collection, sharing, conversion, and training workflows.
A cross-embodiment robotics dataset reference for comparing trajectory scale, robot diversity, and VLA training assumptions.
A large in-the-wild robot manipulation dataset reference for real-world trajectory capture and deployment transfer risk.
TRUELABEL ROUTING
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.