Robot Data Quality Scoring: Why Scores Are Triage, Not Gates

Robot-learning data tooling is rapidly consolidating around common formats, converters, analysis scripts, and inexpensive quality metrics, with MCAP established for ROS 2 logging while training formats such as LeRobot, RLDS, HDF5, and Zarr remain fragmented. Automated scoring methods based on motion smoothness, spectral features, idle time, actuator saturation, and sensor health can efficiently flag noisy or anomalous demonstrations and may improve training results when used selectively, but recent simulated audits indicate that action-only metrics can miss or even favor structurally flawed episodes, such as an early gripper release, and detection accuracy may not predict downstream policy performance. The central gap is therefore a verification loop that connects queryable scores to synchronized multimodal playback, state-aware signals, reversible tagging, and reproducible curation decisions rather than automatic deletion. The article argues that scores should serve as triage tools for human review, particularly as robot datasets grow too large for manual inspection, and presents FiftyOne’s multimodal dataset capabilities and plugin work as infrastructure intended to support this workflow.

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