Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Wendy Carvalho, Meriem Elkoudi, Brendan Hertel, Reza Azadeh

TL;DR

The paper tackles segmenting long robotic demonstrations into sub-tasks for learning from demonstration without manual parameter tuning. It introduces a parameter-free segmentation framework based on cross-correlation between a full-task trajectory and representative sub-task demonstrations, generalized with multiple similarity metrics (CCS, SSE, COS). A sub-task similarity matrix is used to resolve gaps and overlaps, with three algorithms for boundary labeling and preprocessing via smoothing to improve robustness. Empirical results on a simulated handwriting task and a real-world table-setting task show that SSE and COS often outperform the original cross-correlation metric CCS, with smoothing notably boosting performance on real data, demonstrating a lightweight and scalable approach to identifying motion primitives for LfD.

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.

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

TL;DR

The paper tackles segmenting long robotic demonstrations into sub-tasks for learning from demonstration without manual parameter tuning. It introduces a parameter-free segmentation framework based on cross-correlation between a full-task trajectory and representative sub-task demonstrations, generalized with multiple similarity metrics (CCS, SSE, COS). A sub-task similarity matrix is used to resolve gaps and overlaps, with three algorithms for boundary labeling and preprocessing via smoothing to improve robustness. Empirical results on a simulated handwriting task and a real-world table-setting task show that SSE and COS often outperform the original cross-correlation metric CCS, with smoothing notably boosting performance on real data, demonstrating a lightweight and scalable approach to identifying motion primitives for LfD.

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.
Paper Structure (7 sections, 11 equations, 5 figures, 2 tables, 1 algorithm)

This paper contains 7 sections, 11 equations, 5 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Experiments
  5. Segmentation on a Handwriting Task
  6. Segmentation of a Table Setting Task
  7. Conclusions & Future Work

Figures (5)

  • Figure 1: A full-task demonstration of a table setting task, which contains many sub-tasks. The sequencing of sub-tasks is shown.
  • Figure 2: The proposed framework for our parameter-free similarity-based segmentation approach.
  • Figure 3: Segmentation results of the handwriting task using COS, SSE, and CCS as similarity metrics. The representative tasks are displayed in the top row.
  • Figure 4: End-effector position and gripper data for full-task with ground truth indices.
  • Figure 5: COS Segmentation of the full-task, with the sub-tasks listed in order, with and without smoothing. The representative tasks are displayed in the top row.