Getting RoboMIND Running from Scratch: A One-Afternoon Walkthrough

If you want to get into embodied data and you don't have a robot or a collection team, the easiest path is to just download RoboMIND and run through it once, don't collect from scratch yourself.

Embodied intelligence just means giving AI a body so it can reach out and do work, not just chat. A dataset is a bunch of recorded operation processes plus action commands — how the robot moves, how the joints rotate, all recorded in there. RoboMIND was jointly released by the Beijing Humanoid Robot Innovation Center and Peking University's School of Computer Science in December 2024. It contains about 107,000 successful trajectories, covering single-arm Franka and UR5e, dual-arm AgileX, and the "Tiangong" humanoid, equipped with five-finger dexterous hands. In February this year the official word was that downloads passed 2 million, and a few months later it hit 6 million.

First let me explain why I don't recommend collecting from scratch right off the bat.

Dimension	Collect yourself	Use RoboMIND directly
Starting barrier	Need a robot plus teleoperation gear	A computer that can run Python
Time to first usable data	Counted in weeks	Same day
Embodiment coverage	Only your own machine	Single-arm, dual-arm, humanoid all there
Data quality	Depends entirely on who collected it	Officially calibrated, model-validated

Teleoperation means a person uses a controller to remotely operate the robot doing actions, recording while doing it. It's currently the highest-quality collection method, but the equipment and labor costs are right there.

Here's the process.

1. Open the ModelScope community, register an account. Phone number or email both work, takes two minutes.

2. Type RoboMIND in the search box, go into the X-Humanoid dataset page.

3. Check the version clearly. V1.0 and V2.0 are two different things, V2.0 was released later, click into V2.0.

4. The file list is grouped by robot model. Two single-arm, one dual-arm, one humanoid, click open to see subdirectories.

5. Don't rush to download everything. Pick the smallest subset and download that first, pulling the full set will make your hard drive cry.

6. What you download is a compressed archive. After extracting there are usually a few folders, data holds trajectory data, meta holds field descriptions.

7. Set up your Python environment, read meta first, figure out what each column is and what units it's in, then start writing your reading script.

8. Read out one trajectory, plot the joint angles over time as a curve. Once the line moves, that pipeline is working.

There are a few pitfalls.

The first pitfall is thinking you can feed it straight to a model after downloading. If you don't understand the field descriptions, everything after is wasted effort — for example whether some angle is in radians or degrees, guess wrong and what you train is garbage.

The second pitfall is downloading the full set right away. On my end, picking a subset, downloading and extracting took about forty minutes; the full set is another order of magnitude.

The third pitfall is mixing data from different embodiments. Embodiment means the robot's body model — single-arm and dual-arm have different joint dimensions, concatenating them directly will error out, you have to align them first.

The fourth pitfall is in the data itself. These 107,000 trajectories are all successful ones, no failure samples. If you want to train a robot to recover from mistakes, it can't help you, you have to supplement that yourself.

After you get one running, the next step you can try is the ArtVIP set of digital assets. It's high-fidelity articulated objects, like doors that can open and close, knobs that can turn — good for scene validation in a simulation environment. Further on, you can pull a small sample each from RoboMIND and Open X-Embodiment and compare them, to get a feel for the differences in field design and task coverage between data from different sources. The former leans multi-embodiment, multi-task; the latter is the old-school collection Google released back in 2023, with more varied samples. Compare them once and you'll roughly know which side to look for material for your task.