mlsanityData to 14 Oct 2026

The Un-Kidnappable Robot: Acoustic Localization of Sneaking People

arXiv 2310.03743 ↗First posted 5 Oct 2023cs.RO cs.LGcited by 2

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Abstract (arXiv)

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

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 27 of its 56 references that are in the map. First posted on arXiv 5 Oct 2023.

Builds on

27 of 56 references.

References · 27

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15 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Clink! Chop! Thud! -- Learning Object Sounds from Real-World Interactionscited by 0
  2. 2026NeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localizationcited by 0