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Related Experiment Video

Updated: Jul 9, 2026

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

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

Akito Yoshida¹, Rousslan Fernand Julien Dossa¹, Marina Di Vincenzo¹

¹Araya Inc., Tokyo, Japan.

Frontiers in Robotics and AI

|October 13, 2025

Summary

A new benchmark, M4Bench, enables reproducible human-robot interaction (HRI) research. Standardized testing revealed that mouse/keyboard or gamepad controls outperform eye-tracker/EMG for manipulation tasks.

Keywords:

benchmark human-robot interaction multi-agent multimodal shared autonomy

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Last Updated: Jul 9, 2026

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Published on: November 24, 2015

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Published on: May 20, 2020

Area of Science:

Robotics
Human-Computer Interaction
Cognitive Science

Background:

Reproducibility in human-robot interaction (HRI) research is limited by the absence of standardized benchmarks.
Current HRI platforms often lack flexibility for multi-user, multi-robot, and multi-device scenarios.

Purpose of the Study:

To introduce M4Bench, a flexible HRI platform for standardized, reproducible manipulation task research.
To enable quantitative comparisons of different user interfaces and input devices in HRI.

Main Methods:

Developed M4Bench, a multi-user, multi-robot, multi-goal, multi-device manipulation benchmark with a web-based interface.
Integrated support for various input devices including mouse, keyboard, gamepad, eye tracker, and EMG/EEG.
Implemented tracking of HRI metrics like task completion and command selection time.

Main Results:

A user study (n=50) compared five input devices and single-vs. multi-user control.
Performance was significantly worse with eye-tracker + EMG compared to mouse + keyboard or gamepad + gamepad (corrected p < 0.001).

Conclusions:

M4Bench provides a valuable tool for advancing reproducible HRI research.
Input device selection significantly impacts user performance in robotic manipulation tasks.