无需参数的机器人动作分割，用相关性匹配动作片段
Parameter-Free Segmentation of Robot Movements with Cross-Correlation Using Different Similarity Metrics
- 用跨相关性方法匹配动作基元，自动识别复杂动作中的子动作
- 不依赖参数设置，实测在仿真与真实场景中分割准确率高
- 针对机器人运动特性设计相似度度量，适合动作学习与教学场景
机器人常需执行一系列基础动作以完成复杂任务。这些动作可通过教师示范学习，但示范本身可能包含多个子动作，需进行分割。本文提出一种无参数的分割方法，借鉴信号处理中的自相关与互相关思想：通过将代表性的动作基元与复杂示范信号进行跨相关运算，自动定位重复出现的动作片段。关键创新在于采用能捕捉机器人运动特性的相似度度量对相关性过程进行优化。我们在仿真和真实机器人上验证了该框架的有效性，并对比了多种相似度度量的表现，结果表明该方法无需调参即可实现快速、准确的分割。
原文摘要 · Abstract (English)
Often, robots are asked to execute primitive movements, whether as a single action or in a series of actions representing a larger, more complex task. These movements can be learned in many ways, but a common one is from demonstrations presented to the robot by a teacher. However, these demonstrations are not always simple movements themselves, and complex demonstrations must be broken down, or segmented, into primitive movements. In this work, we present a parameter-free approach to segmentation using techniques inspired by autocorrelation and cross-correlation from signal processing. In cross-correlation, a representative signal is found in some larger, more complex signal by correlating the representative signal with the larger signal. This same idea can be applied to segmenting robot motion and demonstrations, provided with a representative motion primitive. This results in a fast and accurate segmentation, which does not take any parameters. One of the main contributions of this paper is the modification of the cross-correlation process by employing similarity metrics that can capture features specific to robot movements. To validate our framework, we conduct several experiments of complex tasks both in simulation and in real-world. We also evaluate the effectiveness of our segmentation framework by comparing various similarity metrics.
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