Improving Human Legibility in Collaborative Robot Tasks through Augmented Reality and Workspace Preparation

TL;DR

This paper introduces a novel method combining workspace arrangement and augmented reality projections to enhance human legibility in collaborative robot tasks, leading to safer and more effective human-robot interactions.

Contribution

It presents an algorithmic approach for workspace configuration and AR projection that improves human behavior clarity and robot goal prediction accuracy.

Findings

Enhanced human behavior legibility in collaborative tasks

Improved robot goal prediction accuracy

Increased safety and task fluency in human-robot collaboration

Abstract

Understanding the intentions of human teammates is critical for safe and effective human-robot interaction. The canonical approach for human-aware robot motion planning is to first predict the human's goal or path, and then construct a robot plan that avoids collision with the human. This method can generate unsafe interactions if the human model and subsequent predictions are inaccurate. In this work, we present an algorithmic approach for both arranging the configuration of objects in a shared human-robot workspace, and projecting ``virtual obstacles'' in augmented reality, optimizing for legibility in a given task. These changes to the workspace result in more legible human behavior, improving robot predictions of human goals, thereby improving task fluency and safety. To evaluate our approach, we propose two user studies involving a collaborative tabletop task with a manipulator robot, and a warehouse navigation task with a mobile robot.