Optimizing Force Signals from Human Demonstrations of In-Contact Motions

TL;DR

This paper investigates methods to optimize force signals from human demonstrations of in-contact motions, aiming to improve robot programming accuracy and human-robot interaction by reducing noise and aligning signals with human intent.

Contribution

It introduces a novel peak detection method and compares various filtering techniques to enhance the quality of force signals in kinesthetic guiding tasks.

Findings

Up to 20% error reduction in signal quality.

Effective filtering improves robot programming usability.

Peak detection handles contact deviations effectively.

Abstract

For non-robot-programming experts, kinesthetic guiding can be an intuitive input method, as robot programming of in-contact tasks is becoming more prominent. However, imprecise and noisy input signals from human demonstrations pose problems when reproducing motions directly or using the signal as input for machine learning methods. This paper explores optimizing force signals to correspond better to the human intention of the demonstrated signal. We compare different signal filtering methods and propose a peak detection method for dealing with first-contact deviations in the signal. The evaluation of these methods considers a specialized error criterion between the input and the human-intended signal. In addition, we analyze the critical parameters' influence on the filtering methods. The quality for an individual motion could be increased by up to \SI{20}{\percent} concerning the error criterion. The proposed contribution can improve the usability of robot programming and the interaction between humans and robots.