相同动作，不同结果：动态布料操作中的变异性

中文摘要 AI 辅助

尽管已知布料在重复快速动态运动下会产生不同结果，即使应用相同轨迹，这种变异性尚未得到系统表征。量化该变异性对于评估学习到的操作策略的可靠性以及模拟再现真实世界行为的程度至关重要。为研究此问题，我们在四个动态任务（其中两个为新任务）中执行相同轨迹共十次，每个任务用三种特性差异极大的布料测试，且测试速度最高达三种，总共记录269次试验。对于所有试验，我们记录布料上的小标记位置及同步立体相机数据。随后，我们形式化不同指标以量化变异性，结果显示在所有测试条件下变异性均显著，多数情况下比机器人和传感噪声固有的可重复性高出1至3个数量级。我们的结果还表明，变异性主要由布料物理特性驱动，且随速度增加而增大。通过在四个经过校准的现代布料模拟器中重放所有试验，我们发现没有一个模拟器能再现我们在真实布料中观察到的变异性幅度，也无法再现其排序。结合我们的分析，我们发布包含同步OptiTrack、视觉和机器人日志及其对应模拟器副本的数据集。

英文摘要

Although cloth is known to exhibit different outcomes under repeated fast dynamic motions, even when the same trajectory is applied, this variability has not yet been systematically characterized. Quantifying it is essential to assess the reliability of learned manipulation policies and the extent to which simulation can reproduce real-world behavior. To study this, we execute the same trajectory ten times across four dynamic tasks, two of which are novel, each tested with three cloths of very different properties and at up to three execution speeds, with a total of 269 recorded rollouts. For all of them, we record small marker positions on the cloth and synchronized stereo camera. We then formalize different metrics to quantify variability, and our results show how it is significant in every test condition, in most cases one to three orders of magnitude above the repeatability inherent to the robot and sensing noise. Our results also show variability is driven mainly by the cloth physical properties but also grows with speed. By replaying all the rollouts in four calibrated modern cloth simulators, we show that none of them can reproduce the variability magnitude we observed in real cloth, nor its ordering. Together with our analysis, we publish the dataset with synchronized OptiTrack, vision and robot logs and their corresponding simulator twins.

发表机构

• Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学机器人与工业自动化研究所）

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