用弧长参数化实现机器人技能合成，无需时间对齐。
Arc-Length-Based Warping for Robot Skill Synthesis from Multiple Demonstrations
- 基于弧长采样重构轨迹，摆脱时间对齐依赖。
- 在变速、停顿频繁的示范中表现更优。
- 适合力控教学等不平滑操作场景。
在机器人领域，从演示学习（LfD）通过多段相同任务的示范来转移技能，通常需借助动态时间规整（DTW）等技术进行时间对齐以提取一致的技能表示。本文提出一种专为机器人轨迹设计的新算法——空间采样（SS），通过提供信号的弧长参数化，实现时间无关的轨迹对齐。该方法避免了传统时间对齐需求，在运动速度极不一致或存在间歇性停顿的场景下显著提升技能表示的准确性和鲁棒性，尤其适用于力控教学中因操作者难以平稳引导末端执行器而产生的非连续动作。为此，我们构建了一个公开可获取的机器人示范数据集，用于测试真实世界中的重复路径追踪任务，其运动规律差异大且含明显启停。实验表明，相比现有最优算法，该方法在轨迹同步性和技能提取质量上均表现更佳。
原文摘要 · Abstract (English)
In robotics, Learning from Demonstration (LfD) aims to transfer skills to robots by using multiple demonstrations of the same task. These demonstrations are recorded and processed to extract a consistent skill representation. This process typically requires temporal alignment through techniques such as Dynamic Time Warping (DTW). In this paper, we consider a novel algorithm, named Spatial Sampling (SS), specifically designed for robot trajectories, that enables time-independent alignment of the trajectories by providing an arc-length parametrization of the signals. This approach eliminates the need for temporal alignment, enhancing the accuracy and robustness of skill representation, especially when recorded movements are subject to intermittent motions or extremely variable speeds, a common characteristic of operations based on kinesthetic teaching, where the operator may encounter difficulties in guiding the end-effector smoothly. To prove this, we built a custom publicly available dataset of robot recordings to test real-world movements, where the user tracks the same geometric path multiple times, with motion laws that vary greatly and are subject to starting and stopping. The SS demonstrates better performances against state-of-the-art algorithms in terms of (i) trajectory synchronization and (ii) quality of the extracted skill.
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