Data Curation for Robotics: Finding Failure Modes Before They Cost You a Deployment

Data curation for robotics aims to identify and correct training-data gaps before they lead to costly or unsafe deployment failures, which often arise from conditions absent or poorly represented in demonstrations rather than from software defects. Robotics requires specialized curation because it combines synchronized multimodal sensor streams with temporally structured action sequences, creating risks such as distribution shift, conflicting task strategies, rare recovery events, sensor drift, and inconsistent annotations. Effective workflows align sensor data, balance action strategies, surface failure patterns through influence-based data attribution, semantic clustering of failure logs, and active learning, then validate changes before retraining. Curation priorities vary across applications including manipulation, locomotion, navigation, humanoid control, and human-robot interaction, but the recommended response is targeted collection or rebalancing rather than indiscriminate data expansion. Success can be assessed through strategy balance, closed-loop performance improvements, edge-case coverage, and lower recurrence of known failures, with platforms such as Encord positioning curation, annotation, and evaluation as a continuous feedback loop.

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