Robot Episode Quality: Scoring Multimodal Data in FiftyOne

FiftyOne’s Episode Quality framework is designed to triage multimodal robot datasets for human review rather than automatically discard demonstrations, because motion and anomaly metrics can identify rough, unusual, or sensor-impaired episodes but cannot determine whether a robot completed the correct task. Using 40 bimanual teleoperation episodes from ABC-130k, it evaluates selected motion signals with SPARC, log dimensionless jerk, RMS jerk, power-spectrum ratios, and idle fraction, while separately checking timestamp-based sensor health indicators such as dropout, desynchronization, clock drift, rate stability, and clipping. Scores are normalized within the current dataset and configuration rather than compared with literature values, assessed independently per signal, and aggregated with a worst-of approach so one problematic arm, gripper, or sensor is not hidden by cleaner channels. Isolation Forest and k-nearest-neighbor models flag dataset-relative outliers, though unusual tasks such as folding may be anomalous without being poor demonstrations. The system distinguishes episode-level rankings from window-level flagged intervals, supports configurable window lengths and frequency cutoffs, and presents results in a FiftyOne panel with charts, per-signal explanations, timeline links, and review tags, enabling users to inspect suspicious clips and make final quality decisions themselves.

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