SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning

• URL: http://arxiv.org/abs/2505.22626v1
• Date: Wed, 28 May 2025 17:45:05 GMT
• Title: SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning
• Authors: Yu Zhang, Yuqi Xie, Huihan Liu, Rutav Shah, Michael Wan, Linxi Fan, Yuke Zhu,
• Abstract summary: Imitation learning advances robot capabilities by enabling the acquisition of diverse behaviors from human demonstrations.<n>Existing robotic curation approaches rely on costly manual annotations and perform curation at a coarse granularity.<n>We introduce SCIZOR, a self-supervised data curation framework that filters out low-quality state-action pairs to improve the performance of imitation learning policies.
• Score: 30.34323856102674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Imitation learning advances robot capabilities by enabling the acquisition of diverse behaviors from human demonstrations. However, large-scale datasets used for policy training often introduce substantial variability in quality, which can negatively impact performance. As a result, automatically curating datasets by filtering low-quality samples to improve quality becomes essential. Existing robotic curation approaches rely on costly manual annotations and perform curation at a coarse granularity, such as the dataset or trajectory level, failing to account for the quality of individual state-action pairs. To address this, we introduce SCIZOR, a self-supervised data curation framework that filters out low-quality state-action pairs to improve the performance of imitation learning policies. SCIZOR targets two complementary sources of low-quality data: suboptimal data, which hinders learning with undesirable actions, and redundant data, which dilutes training with repetitive patterns. SCIZOR leverages a self-supervised task progress predictor for suboptimal data to remove samples lacking task progression, and a deduplication module operating on joint state-action representation for samples with redundant patterns. Empirically, we show that SCIZOR enables imitation learning policies to achieve higher performance with less data, yielding an average improvement of 15.4% across multiple benchmarks. More information is available at: https://ut-austin-rpl.github.io/SCIZOR/

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