Parameter-Free Segmentation of Robot Movements with Cross-Correlation Using Different Similarity Metrics

TL;DR

This paper introduces a parameter-free, signal processing-inspired method for segmenting complex robot movements into primitives using modified cross-correlation with various similarity metrics, validated through experiments.

Contribution

The work presents a novel, parameter-free segmentation approach employing modified cross-correlation with different similarity metrics tailored for robot movements.

Findings

Fast and accurate segmentation without parameters

Effective in both simulation and real-world scenarios

Comparison of multiple similarity metrics shows improved performance

Abstract

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.