Data & Benchmarks

Dataset Curation

Dataset Curation is the process of selecting, filtering, deduplicating, and rebalancing training data to improve downstream model quality. For robot datasets this includes removing failed or idle segments, scoring demonstration quality and operator consistency, balancing task and scene distributions, and verifying calibration and timestamp alignment across sensor streams. Curation choices for large corpora such as Open X-Embodiment materially affect what policies trained on them learn.

Why it matters for physical AI

Noisy or suboptimal demonstrations are cloned along with good ones, so curation quality often bounds imitation learning performance. Systematic filtering and weighting can outperform simply collecting more raw data.