Extending Cobot's Motion Intention Visualization by Haptic Feedback

TL;DR

This paper proposes a novel method to enhance human understanding of cobot motion intentions by using haptic feedback, addressing communication challenges when visual and auditory cues are insufficient.

Contribution

It introduces a haptic feedback system that maps cobot planned motions to tactile patterns, improving transparency of autonomous cobot actions.

Findings

Haptic feedback improves user perception of cobot intentions.

The system enhances trust and predictability in human-robot collaboration.

Haptic cues effectively supplement visual and auditory information.

Abstract

Nowadays, robots are found in a growing number of areas where they collaborate closely with humans. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, supporting people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging for human collaborators to understand and predict their behavior, which is crucial for achieving trust and user acceptance. One significant aspect of predicting cobot behavior is understanding their motion intention and comprehending how they "think" about their actions. Moreover, other information sources often occupy human visual and audio modalities, rendering them frequently unsuitable for transmitting such information. We work on a solution that communicates cobot intention via haptic feedback to tackle this challenge. In our concept, we map planned motions of the cobot to different haptic patterns to extend the visual intention feedback.