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

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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo><</mml:mo></mml:mrow> </mml:math> 0.001). Our software is available at https://github.com/arayabrain/m4bench.

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Publication Details

Journal
Frontiers in Robotics and AI
Published
2025-09-25
DOI
https://doi.org/10.3389/frobt.2025.1528754
Citations
1
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
1.94
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article

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

Hannah Douglas, Kai Arulkumaran, Rousslan Fernand Julien Dossa, Marina Di Vincenzo et al.
1 citations
Frontiers in Robotics and AI
Gaze Tracking and Assistive Technology
1.94
article

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

Hannah Douglas, Kai Arulkumaran, Rousslan Fernand Julien Dossa, Marina Di Vincenzo, Akito Yoshida, Shivakanth Sujit
article en
1 citations

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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo><</mml:mo></mml:mrow> </mml:math> 0.001). Our software is available at https://github.com/arayabrain/m4bench.

Frontiers in Robotics and AIVol. 12
Hoya (Japan) (JP)
Openalex Percentile: Top 12%
Gaze Tracking and Assistive Technology
1.94
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A multi-user multi-robot multi-goal multi-device human-robot interaction manipulation benchmark — Hannah Douglas, Kai Arulkumaran, et al. · Frontiers in Robotics and AI (2025) | TGRS Research Map | TGRS