Explainable Human-Robot Training and Cooperation with Augmented Reality

TL;DR

This paper explores how augmented reality can improve human-robot interaction by making robot behaviors and intentions more understandable, thereby facilitating easier teaching and cooperation in assistive tasks.

Contribution

It introduces three novel AR-based methods to enhance explainability and efficiency in human-robot training and cooperation scenarios.

Findings

AR improves transparency of robot plans and intentions

Enhanced human understanding leads to better task collaboration

AR-based communication supports users with limited mobility

Abstract

The current spread of social and assistive robotics applications is increasingly highlighting the need for robots that can be easily taught and interacted with, even by users with no technical background. Still, it is often difficult to grasp what such robots know or to assess if a correct representation of the task is being formed. Augmented Reality (AR) has the potential to bridge this gap. We demonstrate three use cases where AR design elements enhance the explainability and efficiency of human-robot interaction: 1) a human teaching a robot some simple kitchen tasks by demonstration, 2) the robot showing its plan for solving novel tasks in AR to a human for validation, and 3) a robot communicating its intentions via AR while assisting people with limited mobility during daily activities.