The Un-Kidnappable Robot: Acoustic Localization of Sneaking People
Robotics
2024-05-10 v2 Machine Learning
Abstract
How easy is it to sneak up on a robot? We examine whether we can detect people using only the incidental sounds they produce as they move, even when they try to be quiet. We collect a robotic dataset of high-quality 4-channel audio paired with 360 degree RGB data of people moving in different indoor settings. We train models that predict if there is a moving person nearby and their location using only audio. We implement our method on a robot, allowing it to track a single person moving quietly with only passive audio sensing. For demonstration videos, see our project page: https://sites.google.com/view/unkidnappable-robot
Keywords
Cite
@article{arxiv.2310.03743,
title = {The Un-Kidnappable Robot: Acoustic Localization of Sneaking People},
author = {Mengyu Yang and Patrick Grady and Samarth Brahmbhatt and Arun Balajee Vasudevan and Charles C. Kemp and James Hays},
journal= {arXiv preprint arXiv:2310.03743},
year = {2024}
}
Comments
ICRA 2024 camera ready