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RoboMNIST: A Multimodal Dataset for Multi-Robot Activity Recognition Using WiFi Sensing, Video, and Audio.

Kian Behzad¹, Rojin Zandi¹, Elaheh Motamedi¹

¹Department of Electrical & Computer Engineering, Northeastern University, Boston, MA, USA.

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|February 22, 2025

Summary

This summary is machine-generated.

A new multimodal dataset for multi-robot activity recognition (MRAR) uses WiFi Channel State Information (CSI), video, and audio. This approach enhances robotic perception and autonomous systems by leveraging existing WiFi signals for environmental sensing.

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Area of Science:

Robotics
Artificial Intelligence
Sensor Networks

Background:

Multi-robot activity recognition (MRAR) is crucial for advanced autonomous systems.
Existing methods often require dedicated sensors, increasing deployment costs and complexity.
Leveraging signals of opportunity, such as WiFi, offers a cost-effective sensing solution.

Purpose of the Study:

Introduce a novel multimodal dataset for MRAR.
Integrate WiFi Channel State Information (CSI), video, and audio data for enhanced robotic perception.
Facilitate the development of robust and accurate MRAR systems.

Main Methods:

Collected data using two Franka Emika robotic arms.
Integrated WiFi CSI, video, and audio streams from multiple sensors.
Utilized signals of opportunity from existing WiFi infrastructure for environmental sensing.

Main Results:

Developed a comprehensive multimodal dataset for MRAR.
Demonstrated the potential of combining CSI, visual, and auditory data for improved recognition.
Enabled a holistic understanding of robotic environments for advanced operations.

Conclusions:

The novel dataset advances robotic perception and autonomous systems.
Repurposing WiFi signals for sensing provides a valuable resource for developing sophisticated decision-making capabilities.
The multimodal approach enhances robustness and accuracy in dynamic environments.