Introducing Calibra: Open-source Robot Dataset Observability

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Hey everyone!

I’d like to share Calibra which is an open-source toolkit for robot dataset observability.

I realized that while robot learning has excellent tooling for training policies, it’s still difficult to understand the quality of a dataset before training. Calibra aims to help by auditing datasets, identifying potential quality issues, building quality-aware coresets, and estimating training outcomes.

As part of the first public release, we’ve launched:

  • Robot Dataset Health Check — an interactive Space for auditing LeRobot datasets in seconds.

  • A benchmark of 30 public LeRobot datasets with health scores, quality diagnostics, and community comparisons.

The full toolkit also includes quality-aware coreset selection, training outcome prediction, and a Python API/CLI.

Try it: Robot Dataset Health Check - a Hugging Face Space by omert27

Source code: GitHub - omertt27/Calibra: Dataset observability and coreset selection for robotics imitation learning · GitHub

I’d really appreciate feedback from the Hugging Face and robotics communities.

Some questions I’d love your thoughts on:

  • Which dataset quality metrics would be most useful for your workflow?

  • Are there important diagnostics that are missing?

  • If you work with LeRobot datasets, would you use something like this before training?

Thanks for taking a look, i would be appreciated any feedbacks.

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