Segmentation of Robot Movements using Position and Contact Forces

TL;DR

This paper presents a novel method for segmenting robot movements by combining position clustering with contact force analysis, verified through experiments on industrial robots.

Contribution

It introduces a hybrid segmentation approach using Gaussian mixture models and Kalman filters to improve accuracy in autonomous robot movement segmentation.

Findings

Effective segmentation of robot movements demonstrated

Kalman filter improves contact force change detection

Method validated on industrial robot experiments

Abstract

In this paper, a method for autonomous segmentation of demonstrated robot movements is proposed. Position data is clustered into Gaussian mixture models (GMMs), and an initial set of segments is identified from the Gaussian basis functions. A Kalman filter is used to detect sudden changes in the contact force/torque measurements, and this is used to update and verify the initial segmentation points. The segmentation method is verified experimentally on an industrial robot.