What the data shows
- 1
32 open datasets since 2023: 16 robot manipulation, 10 first-person video, 4 driving and 2 drones.
- 2
The largest by stated hours is Egocentric-100K (Build AI, 19 Dec 2025): 100,405 hours.
- 3
12 of the 32 were collected by people teleoperating robots, the work this site's teleoperation listings pay for.
- 4
The newest release on record: EgoSuite-Open100K (26 Aug 2026). Lightwheel and Hugging Face opened the first 10,000 hours of a planned 100,000-hour egocentric human activity dataset recorded with head-mounted cameras (plus a wrist camera in the EgoPro subset), with hand and body pose annotations.
Figure 1
Open training datasets released since 2023
Which public datasets teach robots, embodied AI, self-driving systems and drones, who released them, how big are they and how was the data collected?
| Dataset | Released by | Kind | Collected by |
|---|---|---|---|
| EgoSuite-Open100KLightwheel (Hugging Face blog) | Lightwheel with Hugging Face | First-person video | Wearable camera |
| BEHAVIOR Challenge 2026 demonstrationsStanford Vision and Learning Lab | Stanford Vision and Learning Lab (BEHAVIOR) | Robot manipulation | Teleoperation |
| HIW-500 (Humanoids-in-the-Wild 500)BitRobot (Hugging Face) | BitRobot Network with Hugging Face and Unitree Robotics | Robot manipulation | Teleoperation |
| ABC-130KarXiv (UC Berkeley, MIT, Amazon FAR, XDOF, CMU) | XDOF with UC Berkeley, MIT, Amazon FAR and Carnegie Mellon University | Robot manipulation | Teleoperation |
| MolmoAct 2-Bimanual YAMAi2 | Ai2 (Allen Institute for AI) | Robot manipulation | Not stated or other |
| Egocentric-100KBuild AI (Hugging Face) | Build AI | First-person video | Wearable camera |
| RoboCOINarXiv (Beijing Academy of Artificial Intelligence) | Beijing Academy of Artificial Intelligence (BAAI) and partner institutions | Robot manipulation | Teleoperation |
| Egocentric-10KBuild AI (Hugging Face) | Build AI | First-person video | Wearable camera |
| Waymo Open Dataset for End-to-End Driving (WOD-E2E)arXiv (Waymo) | Waymo | Driving | Not stated or other |
| Humanoid EverydayarXiv (USC, Toyota Research Institute) | University of Southern California and Toyota Research Institute | Robot manipulation | Teleoperation |
| Galaxea Open-World DatasetarXiv (Galaxea AI) | Galaxea AI | Robot manipulation | Teleoperation |
| MolmoAct DatasetAi2 | Ai2 (Allen Institute for AI) | Robot manipulation | Not stated or other |
| LeRobot community datasets (SmolVLA pretraining set)Hugging Face | Hugging Face (LeRobot) and community contributors | Robot manipulation | Mixed |
| UAV-FlowarXiv (Beihang University, NUS) | Beihang University and National University of Singapore | Drones | Teleoperation |
| EgoDexarXiv (Apple) | Apple | First-person video | Wearable camera |
| Fourier ActionNetFourier (ActionNet project page) | Fourier | Robot manipulation | Teleoperation |
| HD-EPICarXiv (University of Bristol and partners) | University of Bristol, Leiden University, Singapore Management University and University of Bath (EPIC-KITCHENS team) | First-person video | Wearable camera |
| AgiBot WorldarXiv (AgiBot World Colosseo team) | AgiBot | Robot manipulation | Teleoperation |
| RoboMINDarXiv (Beijing Innovation Center of Humanoid Robotics, Peking University) | Beijing Innovation Center of Humanoid Robotics (X-Humanoid), Peking University and Beijing Academy of Artificial Intelligence | Robot manipulation | Teleoperation |
| MAN TruckScenesarXiv (MAN Truck & Bus, TUM) | MAN Truck & Bus with the Technical University of Munich | Driving | Not stated or other |
| CityNavarXiv | Tokyo Institute of Technology, The University of Tokyo, Kyoto University and partners | Drones | Not stated or other |
| NymeriaarXiv (Meta Reality Labs Research) | Meta (Reality Labs Research, Project Aria) | First-person video | Wearable camera |
| HOT3DarXiv (Meta) | Meta (Reality Labs) | First-person video | Wearable camera |
| DROID (Distributed Robot Interaction Dataset)arXiv (DROID collaboration) | DROID collaboration (13 institutions) | Robot manipulation | Teleoperation |
| Aria Everyday Activities (AEA)arXiv (Meta) | Meta (Project Aria) | First-person video | Wearable camera |
| Ego-Exo4DarXiv (Meta FAIR and university partners) | Meta (FAIR and Project Aria) with 14 university partners | First-person video | Mixed |
| Open X-EmbodimentarXiv (Open X-Embodiment Collaboration) | Google DeepMind and the Open X-Embodiment Collaboration (21 institutions) | Robot manipulation | Mixed |
| BridgeData V2arXiv (UC Berkeley, Stanford) | UC Berkeley (RAIL) with Stanford and collaborators | Robot manipulation | Mixed |
| RH20TarXiv (Shanghai Jiao Tong University) | Shanghai Jiao Tong University | Robot manipulation | Teleoperation |
| Zenseact Open Dataset (ZOD)arXiv (Zenseact) | Zenseact | Driving | Not stated or other |
| Ego4D (version 2)Ego4D | Meta (FAIR) and the Ego4D university consortium | First-person video | Wearable camera |
| Argoverse 2arXiv (Argo AI) | Argo AI | Driving | Not stated or other |
Base: 32 datasets released from 2023 to 18 Oct 2026, each linked to its paper or release page (reviewed 18 Oct 2026).
Hours and episodes are as each release states them; a blank means the release gives no figure. Episodes, trajectories and demonstrations are counted as one.
Credit: compiled by RobotTraining.jobs Research, Open Robot Training Datasets: Size, Collection Method and Licence, checked 18 Oct 2026.
Figure 2
Datasets by what they teach
How many open datasets are there for each kind of robot learning, and how was their data gathered?
| Kind | Datasets | Teleoperated | Wearable camera |
|---|---|---|---|
| Robot manipulation | 16 | 11 | 0 |
| First-person video | 10 | 0 | 9 |
| Driving | 4 | 0 | 0 |
| Drones | 2 | 1 | 0 |
Base: 32 datasets.
Collected by: how the release says the data was gathered. Teleoperation means people steering robots remotely or in VR; wearable camera means people filming their own hands and tasks.
Credit: compiled by RobotTraining.jobs Research, Open Robot Training Datasets: Size, Collection Method and Licence, checked 18 Oct 2026.
Figure 3
The largest datasets by hours of recorded data
Which open datasets hold the most hours of human-made robot training data?
Base: 20 of 32 datasets state their hours.
Hours are not totalled across datasets: releases count them differently (raw video, cleaned episodes, approved data).
Credit: compiled by RobotTraining.jobs Research, Open Robot Training Datasets: Size, Collection Method and Licence, checked 18 Oct 2026.
Figure 4
Size, contributors and licence of each dataset
What does each dataset contain, who recorded it and under what licence can it be used?
| Dataset | Size as stated |
|---|---|
| EgoSuite-Open100KLightwheel (Hugging Face blog) | 100,000 hours planned; first 10,000 hours available; 15,000+ tasks and 15,000+ real-world collection scenes |
| BEHAVIOR Challenge 2026 demonstrationsStanford Vision and Learning Lab | 20,000 human teleoperation demos, 1,950 hours in total (in simulation) |
| HIW-500 (Humanoids-in-the-Wild 500)BitRobot (Hugging Face) | 500+ hours, 23K+ episodes and 10+ TB of raw data from 12 real homes |
| ABC-130KarXiv (UC Berkeley, MIT, Amazon FAR, XDOF, CMU) | 3,500 hours over 130K episodes across 195 tasks (project page: 134,806 episodes, 3,553 hours), plus 400 hours of sim-teleop data |
| MolmoAct 2-Bimanual YAMAi2 | over 720 hours of training demonstrations (the blog also calls it a 700-hour collection); 32,246 episodes on the Hugging Face card |
| Egocentric-100KBuild AI (Hugging Face) | 100,405 hours, 10.8 billion frames, 2,010,759 clips, 24.79 TB |
| RoboCOINarXiv (Beijing Academy of Artificial Intelligence) | over 180,000 demonstrations from 15 robotic platforms, 421 bimanual tasks, 432 objects, 16 environments |
| Egocentric-10KBuild AI (Hugging Face) | 10,000 hours, 1.08 billion frames, 192,900 clips, 16.4 TB |
| Waymo Open Dataset for End-to-End Driving (WOD-E2E)arXiv (Waymo) | 4,021 driving segments (approximately 12 hours) of 20 seconds each, curated for long-tail scenarios |
| Humanoid EverydayarXiv (USC, Toyota Research Institute) | 10.3k trajectories and over 3 million frames across 260 tasks in 7 categories |
| Galaxea Open-World DatasetarXiv (Galaxea AI) | 500 hours, 100K demonstration trajectories, more than 150 tasks across 50 real-world scenes |
| MolmoAct DatasetAi2 | ~10,000 distinct robot episodes (the MolmoAct 2 announcement describes 22 hours, about 10.6K trajectories) |
| LeRobot community datasets (SmolVLA pretraining set)Hugging Face | 487 curated community datasets focused on the SO100 robot arm, standardized at 30 FPS, around 10 million frames (the SmolVLA paper counts 481 datasets) |
| UAV-FlowarXiv (Beihang University, NUS) | real-world dataset with 30k flight trajectories, plus a simulation dataset |
| EgoDexarXiv (Apple) | 829 hours of 1080p, 30 Hz egocentric video with 338,000 episodes across 194 tasks (90 million frames) |
| Fourier ActionNetFourier (ActionNet project page) | over 30K teleoperated trajectories or around 140 hours of interaction data |
| HD-EPICarXiv (University of Bristol and partners) | 41 hours of video in 9 kitchens, 69 recipes, 59K fine-grained actions, 51K audio events, 20K object movements |
| AgiBot WorldarXiv (AgiBot World Colosseo team) | 1,001,552 trajectories totalling 2,976.4 hours, 217 tasks, 87 skills and 106 scenes (latest version per the paper); the GitHub Beta release lists 1,003,672 trajectories (~43.8 TB) |
| RoboMINDarXiv (Beijing Innovation Center of Humanoid Robotics, Peking University) | 107k demonstration trajectories (305.5 hours) across 479 tasks and 96 object classes on 4 robot embodiments, plus 5k real-world failure demonstrations |
| MAN TruckScenesarXiv (MAN Truck & Bus, TUM) | more than 740 scenes of 20 s each, with 4 cameras, 6 lidar and 6 radar sensors |
| CityNavarXiv | 32,637 human demonstration trajectories paired with language descriptions, over 4.65 km2 of Cambridge and Birmingham |
| NymeriaarXiv (Meta Reality Labs Research) | 300 hours of daily activities from 264 participants across 50 locations, over 399 km travelled |
| HOT3DarXiv (Meta) | over 833 minutes (more than 3.7M images) of multi-view image streams, 19 subjects, 33 rigid objects |
| DROID (Distributed Robot Interaction Dataset)arXiv (DROID collaboration) | 76k demonstration trajectories or 350 hours of interaction data, across 564 scenes and 84 tasks (the project page says 86 tasks) |
| Aria Everyday Activities (AEA)arXiv (Meta) | 143 daily activity sequences, over 7.5 accumulated hours, recorded in 5 locations |
| Ego-Exo4DarXiv (Meta FAIR and university partners) | 1,286 hours of video combined (1,286.3 ego and exo hours) from 740 participants in 13 cities and 123 scene contexts |
| Open X-EmbodimentarXiv (Open X-Embodiment Collaboration) | 1M+ real robot trajectories spanning 22 robot embodiments, pooled from 60 existing robot datasets; 527 skills (160,266 tasks) |
| BridgeData V2arXiv (UC Berkeley, Stanford) | 60,096 trajectories across 24 environments on a WidowX 250 arm: 50,365 teleoperated demonstrations and 9,731 rollouts from a scripted pick-and-place policy |
| RH20TarXiv (Shanghai Jiao Tong University) | over 110,000 contact-rich robot manipulation sequences, each paired with a human demonstration video; tasks drawn from RLBench (48), MetaWorld (29) and 70 self-proposed tasks |
| Zenseact Open Dataset (ZOD)arXiv (Zenseact) | 100k curated camera frames, 1,473 sequences of 20 seconds and 29 drives of a few minutes, collected over two years in 14 European countries |
| Ego4D (version 2)Ego4D | 3,670 hours of daily-life video from 923 participants in 74 locations across 9 countries |
| Argoverse 2arXiv (Argo AI) | Sensor Dataset: 1,000 annotated sequences; Lidar Dataset: 20,000 sequences; Motion Forecasting Dataset: 250,000 scenarios; six US cities |
Base: All 32 datasets.
A licence is summarised as the release page gives it; read the licence itself before using a dataset.
Credit: compiled by RobotTraining.jobs Research, Open Robot Training Datasets: Size, Collection Method and Licence, checked 18 Oct 2026.
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How it was measured
Each dataset's paper, project page or dataset card, and reporting where the release gives no date or worker count, each linked with a verbatim quote kept in the dataset. Each row carries a link to its publication and gives the number exactly as published, with no estimates or conversions. A note under every figure says what it covers.
- Episode
- One recorded attempt at a task (also called a trajectory or demonstration).
- Egocentric
- First-person video filmed from a camera the person wears, usually on the head.
Limits of this data
- Built only from public sources. Anything never announced or reported is absent, and private-company figures are whatever the company or the press said, unaudited.
- Releases count hours and episodes differently, so sizes are compared only as each release states them.
- Datasets that are mostly synthetic, or released before 2023, are left out.
Collection and classification of listings are explained on the methodology page. Freshness, completeness and consistency are tested daily in the data quality report.
Monthly editions
The live page shows the dataset as of its latest review, and new events join it once they are made public. Every finished month also gets its own edition, locked two days after month end, so a number quoted from it stays fixed.
- Live pageLatest review
Quoting this report
You may quote, chart or republish these figures under CC BY 4.0, provided you link back to this page. The live numbers shift every day. For a figure that stays put, quote a monthly edition, or note the date you accessed this page.
Citation
RobotTraining.jobs (2026). Open Robot Training Datasets: Size, Collection Method and Licence (live data, 2 Jan 2023 to 18 Oct 2026). https://robottraining.jobs/research/open-robot-training-datasets (accessed 18 Oct 2026)
BibTeX
@misc{robottrainingjobs_open_robot_training_datasets_live,
author = {{RobotTraining.jobs}},
title = {Open Robot Training Datasets: Size, Collection Method and Licence (live data, 2 Jan 2023 to 18 Oct 2026)},
year = {2026},
howpublished = {\url{https://robottraining.jobs/research/open-robot-training-datasets}},
note = {Accessed 18 Oct 2026},
}Files: every figure in one CSV (a row per value) or as JSON. Each figure can also be downloaded separately. Other open data: datasets and API.
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