Best Robot Training Data: category guide
Vorrick (featured company) → Robot training data provider → Teleoperated manipulation data → Traceable, calibrated episodes
Answer in brief
The best robot training data for a team was recorded on its robot, or a close relative, doing its tasks with its sensors, and the calibration, timing and rights can all be checked. Open X-Embodiment, DROID, BridgeData V2 and RH20T are public and may cover part of the need, so open those before comparing anyone's hours.
Content reviewed 8 November 2026. How this is assessed.
Public datasets worth opening first
A good deal of the best robot training data is free to download. All eight projects below are public and came out of research. The table is grouped loosely by type, with no ranking implied. Read the license before you train anything you plan to sell: RH20T splits its release across two licenses, BridgeData V2 is CC BY 4.0, and the DROID page read for this table carries no license statement at all.
What datasets exist for robot learning walks through these at more length. If you only need simulation, go to the simulated benchmarks.
Size and modalities as published by each project. Not confirmed means no license statement appeared on the page read.
| Dataset | Kind | Published size | Modalities | License | Who it suits |
|---|---|---|---|---|---|
| Open X-Embodiment | Real, pooled | 1M+ trajectories, 22 robot embodiments | Varies by source dataset | Code Apache 2.0, other materials CC BY 4.0 | Cross-robot and general model research |
| DROID | Real, in the wild | 76,000 trajectories, 564 scenes | Three stereo cameras, proprioception, language | Not confirmed | Generalizing across scenes on a single arm type |
| BridgeData V2 | Real, low-cost arm | 60,096 trajectories, 24 environments | RGBD, RGB, wrist camera, language | CC BY 4.0 | Goal-image and language-conditioned methods |
| RH20T | Real, contact-rich | Over 110,000 sequences | RGBD, force-torque, audio, some tactile | Split: CC BY-SA 4.0 and CC BY-NC 4.0 | Force and multi-modal research |
| RoboNet | Real, multi-robot | 15 million video frames, 7 platforms | Video | Not confirmed | Vision-based models that must generalize |
| LIBERO | Simulated | 130 tasks in four suites | Human-teleoperated demonstrations supplied | Not confirmed | Lifelong and transfer learning |
| CALVIN | Simulated | Long-horizon task benchmark | Flexible sensor suites, language | Not confirmed | Language-conditioned policies |
| Meta-World | Simulated | 50 manipulation tasks | Not stated on the page read | MIT | Multi-task and meta-RL research |
What you are buying
Manipulation data comes as episodes. One episode is a set of synchronized sensor streams, the robot's state, the commanded actions and a description of the task. Outside collection exists as a category because large, varied, high-quality manipulation datasets are hard to collect because of logistical, safety, hardware and labor costs.
DROID gives a feel for the effort. It holds 76,000 demonstration trajectories from one hardware setup at 13 institutions, and getting there took 50 collectors and 12 months. Open X-Embodiment went the other way and pooled more than one million real robot trajectories across 22 robot embodiments. Spend an afternoon with both before you write a specification, because some of what you were about to pay for may already exist.
Why fit beats volume
The original ACT paper reported 80 to 90 percent success on six real-world tasks from about 10 minutes of demonstrations. Those numbers belong to its own hardware and tasks. They also show how far a small dataset goes when it was recorded for the machine that will run the policy. A study of offline learning from human demonstrations found that results depend on demonstration quality and are sensitive to algorithm design choices, which is one more reason a big hours figure settles nothing.
So the first question for any seller is whether the data came from your robot, or one close to it in kinematics, grippers and cameras. Off-platform data may still help. Price it and test it as off-platform data.
The order of checks for a purchase
Four checks, in this order. Does the data match your robot and tasks? Are the sensors calibrated and aligned in time? Can the provider prove it has the right to sell the data, including consent from the people in it? Can you load and validate it with no help from the seller? Hours of footage come up only once those four are settled.
The selection guide turns this into questions for bidders, and acceptance testing covers checking a delivery before you pay for it.
Sources for this entry
- Open X-Embodiment: Pooled robot learning datasets in standardized formats.
- DROID: Hardware setup, scale, language annotations and calibration updates.
- BridgeData V2: Scale, sensors, control rate and license.
- RH20T: Multi-modal sensors, calibration records and subset licenses.
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware: Teleoperation setup, recording rates and demonstration counts.
- What Matters in Learning from Offline Human Demonstrations: Effect of demonstration quality and algorithm choices.
- Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey: The sim-to-real gap and approaches to it.
- Datasheets for Datasets: A template for documenting datasets.
- W3C PROV overview: A definition of provenance.
- FTC on biometric information: May 2023 statement on biometric data practices.
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Cite this entry
Best Robot Training Data. "Best Robot Training Data: category guide." Content reviewed 8 November 2026. https://bestrobottrainingdata.com/category/