AUTHOR=Yoshida Akito , Dossa Rousslan Fernand Julien , Di Vincenzo Marina , Sujit Shivakanth , Douglas Hannah , Arulkumaran Kai 

TITLE=A multi-user multi-robot multi-goal multi-device human-robot interaction manipulation benchmark

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2025.1528754

DOI=10.3389/frobt.2025.1528754

ISSN=2296-9144

ABSTRACT=One weakness of human-robot interaction (HRI) research is the lack of reproducible results, due to the lack of standardised benchmarks. In this work we introduce a multi-user multi-robot multi-goal multi-device manipulation benchmark (M4Bench), a flexible HRI platform in which multiple users can direct either a single—or multiple—simulated robots to perform a multi-goal pick-and-place task. Our software exposes a web-based visual interface, with support for mouse, keyboard, gamepad, eye tracker and electromyograph/electroencephalograph (EMG/EEG) user inputs. It can be further extended using native browser libraries or WebSocket interfaces, allowing researchers to add support for their own devices. We also provide tracking for several HRI metrics, such as task completion and command selection time, enabling quantitative comparisons between different user interfaces and devices. We demonstrate the utility of our benchmark with a user study (n = 50) conducted to compare five different input devices, and also compare single-vs. multi-user control. In the pick-and-place task, we found that users performed worse when using the eye tracker + EMG device pair, as compared to mouse + keyboard or gamepad + gamepad, over four quantitative metrics (corrected p < 0.001). Our software is available at https://github.com/arayabrain/m4bench.