The Un-Kidnappable Robot: Acoustic Localization of Sneaking People

October 05, 2023 Β· Declared Dead Β· πŸ› IEEE International Conference on Robotics and Automation

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Authors Mengyu Yang, Patrick Grady, Samarth Brahmbhatt, Arun Balajee Vasudevan, Charles C. Kemp, James Hays arXiv ID 2310.03743 Category cs.RO: Robotics Cross-listed cs.LG Citations 1 Venue IEEE International Conference on Robotics and Automation Last Checked 4 months ago
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
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