Through the Clutter: Exploring the Impact of Complex Environments on the Legibility of Robot Motion
Melanie Schmidt-Wolf, Tyler Becker, Denielle Oliva, Monica Nicolescu,, and David Feil-Seifer

TL;DR
This paper introduces a new measure for environment clutter and a novel motion planner to improve robot motion legibility in cluttered settings, validated through experiments with Baxter robots.
Contribution
It presents a clutter-based entropic measure and a potential fields-based motion planner, demonstrating improved legibility in cluttered environments over existing methods.
Findings
Significant improvement in legibility with the proposed planner
Performance varies notably between cluttered and uncluttered environments
Highlights the need for further research in cluttered environment scenarios
Abstract
The environments in which the collaboration of a robot would be the most helpful to a person are frequently uncontrolled and cluttered with many objects present. Legible robot arm motion is crucial in tasks like these in order to avoid possible collisions, improve the workflow and help ensure the safety of the person. Prior work in this area, however, focuses on solutions that are tested only in uncluttered environments and there are not many results taken from cluttered environments. In this research we present a measure for clutteredness based on an entropic measure of the environment, and a novel motion planner based on potential fields. Both our measures and the planner were tested in a cluttered environment meant to represent a more typical tool sorting task for which the person would collaborate with a robot. The in-person validation study with Baxter robots shows a significant…
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Taxonomy
TopicsRobot Manipulation and Learning
