How to Curate Robot Episode Data at Scale?

Robot episode data curation is the process of selecting, ranking, and preparing complete task demonstrations for training, distinct from labeling the actions and events within each episode. The article argues that larger robotics datasets do not inherently produce better Vision-Language-Action policies, since redundant, low-quality, inconsistent, or unlabeled failed demonstrations can dilute or harm training signals. Common issues include semantically duplicate trajectories, vague task instructions, missing segmentation of multi-step tasks, incorrect object labels, incompatible data from different robot embodiments, and failures recorded as successes. A scalable curation workflow therefore combines embedding-based deduplication, model-based quality scoring, explicit treatment of failed or ambiguous episodes, normalization of action spaces and coordinate systems across sources, and a continuous feedback loop that prioritizes difficult deployment cases for future training. Manual review can support small datasets, but datasets in the thousands or millions require automated similarity search, targeted human review, and continuous model-informed filtering, particularly for VLA fine-tuning, cross-embodiment learning, and expensive dexterous or humanoid robotics demonstrations.

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