MinInter：在模仿学习的数据增强中最小化轨迹插值
MinInter: Minimizing Trajectory Interpolation During Data Augmentation for Imitation Learning
AI总结：
提出MinInter方法，通过选择需要最少插值的源演示来生成高质量合成轨迹，在MimicGen基准的12个操作任务中显著提升数据生成成功率和策略成功率。
摘要：
模仿学习使机器人能够从演示中获取复杂的操作技能，但其有效性受到收集高质量数据成本的限制。轨迹级数据增强方法通过在不同初始状态下重组专家演示来缓解这一挑战。然而，此类方法通常在不连续部分之间插入插值或其他非专家过渡段，这些非专家段可能降低生成数据的质量。本文介绍了最小化插值（MinInter），一种有效的轨迹选择方法，对于每个采样的初始配置，选择需要最少插值以形成完整轨迹的源演示。通过在数据生成过程中显式最小化插值，MinInter生成更高质量的合成演示，同时与现有数据生成框架兼容。在MimicGen基准的12个操作任务（26个变体）上的实验表明，MinInter一致地提高了数据生成成功率和策略成功率，在接触密集、长时域和高方差设置中提升最大。与最近的SkillGen框架相比，MinInter尽管概念简单，却实现了更高的策略成功率，凸显了插值最小化对数据增强的价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
