Calibra v0.11 makes robotics dataset analysis more accurate and reliable.
This release adds dataset profiles, so Calibra can adapt its analysis and pruning logic to how a dataset was actually collected instead of applying one global threshold everywhere.
It also fixes and verifies key workflows across LeRobot, Isaac Lab, GR00T, HDF5, dataset export, calibration baselines, duplicate-frame detection, and deterministic pruning.
The result: cleaner dataset decisions and training-ready exports.
I built Calibra a layer for robotics dataset quality and intelligent data curation
We are exploring how teams can automatically audit data quality, detect problematic demonstrations.Therefore you can build smaller and high-quality training sets before spending GPU time. So you can reduce your training costs up to %70.
Calibra helps robotics teams audit dataset integrity, measure quality and coverage, and build smaller training sets. v0.8 adds measured training experiments: record real GPU-hours, wall-clock time, energy, and policy performance, then compare Full vs Random vs Calibra across retention levels.
Calibra: Train with Less DataTrain robot policies with up to 75% less data
