MinInter: 模倣学習のデータ拡張における軌道補間の最小化
MinInter: Minimizing Trajectory Interpolation During Data Augmentation for Imitation Learning
模倣学習のデータ拡張で、初期状態ごとに補間が最小となる実演を選ぶ手法MinInterを提案し、生成データの質とポリシー成功率を向上させた。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Qingyang Wang, Xingang Liu, Changwei Yao, Zikai Ouyang, Junwei Liu, Haibo Lu, Wei Zhang
分類: cs.RO
原文アブストラクト
Imitation learning enables robots to acquire complex manipulation skills from demonstrations, but its effectiveness is limited by the cost of collecting high-quality data. Trajectory-level data augmentation methods alleviate this challenge by recombining expert demonstrations under varied initial states. However, such methods typically insert interpolations or other non-expert transition segments between disjoint parts, and such non-expert segments could reduce the quality of the generated data. This paper introduces Minimizing Interpolation (MinInter), an effective trajectory selection method that, for each sampled initial configuration, chooses the source demonstration requiring the least interpolation to form a complete trajectory. By explicitly minimizing interpolations during data generation, MinInter produces higher-quality synthetic demonstrations while remaining compatible with existing data generation frameworks. Experiments on 12 manipulation tasks with 26 variants from the MimicGen benchmark show that MinInter consistently improves both data generation success rates and policy success rates, with the largest gains on contact-rich, long-horizon and high-variance settings. Compared to the recent SkillGen framework, MinInter achieves higher policy success rates despite its conceptual simplicity, underscoring the value of interpolation minimization for data augmentation.
