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

TL;DR

This paper presents RoboMNIST, a comprehensive multimodal dataset combining WiFi CSI, video, and audio data from two robotic arms to advance multi-robot activity recognition and environmental perception.

Contribution

It introduces a novel multimodal dataset utilizing WiFi, video, and audio signals for multi-robot activity recognition, leveraging existing infrastructure for enhanced robotic perception.

Findings

Multimodal data improves activity recognition accuracy.

WiFi CSI provides detailed environmental sensing.

The dataset enables advanced autonomous robotic operations.

Abstract

We introduce a novel dataset for multi-robot activity recognition (MRAR) using two robotic arms integrating WiFi channel state information (CSI), video, and audio data. This multimodal dataset utilizes signals of opportunity, leveraging existing WiFi infrastructure to provide detailed indoor environmental sensing without additional sensor deployment. Data were collected using two Franka Emika robotic arms, complemented by three cameras, three WiFi sniffers to collect CSI, and three microphones capturing distinct yet complementary audio data streams. The combination of CSI, visual, and auditory data can enhance robustness and accuracy in MRAR. This comprehensive dataset enables a holistic understanding of robotic environments, facilitating advanced autonomous operations that mimic human-like perception and interaction. By repurposing ubiquitous WiFi signals for environmental sensing, this dataset offers significant potential aiming to advance robotic perception and autonomous systems. It provides a valuable resource for developing sophisticated decision-making and adaptive capabilities in dynamic environments.