基于弧长的变形用于多示教机器人技能合成
Arc-Length-Based Warping for Robot Skill Synthesis from Multiple Demonstrations
AI总结：
本文提出基于弧长的变形方法，用于提升多示教机器人技能合成的轨迹同步和技能提取质量。
摘要：
在机器人领域，学习从示范（LfD）旨在通过使用相同任务的多个示范将技能转移到机器人上。这些示范被记录并处理以提取一致的技能表示。此过程通常需要通过动态时间变形（DTW）等技术进行时间对齐。在本文中，我们考虑了一种新的算法，称为空间采样（SS），专门针对机器人轨迹设计，能够通过提供信号的弧长参数化来实现轨迹的时间独立对齐。这种方法消除了对时间对齐的需求，提高了技能表示的准确性和鲁棒性，尤其是在记录的运动可能受间歇运动或极不规则速度影响时，这在基于动力学教学的操作中是常见的特征。为了证明这一点，我们构建了一个自定义的公开可用机器人记录数据集来测试现实中的运动，其中用户多次跟踪相同的几何路径，运动规律差异很大，并且会受到起始和停止的影响。SS在轨迹同步和提取的技能质量方面比最先进的算法表现更好。
英文摘要：
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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