# The Un-Kidnappable Robot: Acoustic Localization of Sneaking People

arXiv: https://arxiv.org/abs/2310.03743 · HTML: https://mlsanity.com/paper/2310.03743/

## 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 (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Joint 3D Proposal Generation and Object Detection from View Aggregation](https://mlsanity.com/paper/1712.02294/) (2017)
- [Features for Multi-Target Multi-Camera Tracking and Re-Identification](https://mlsanity.com/paper/1803.10859/) (2018)
- [Scale Match for Tiny Person Detection](https://mlsanity.com/paper/1912.10664/) (2019)
- [BatVision: Learning to See 3D Spatial Layout with Two Ears](https://mlsanity.com/paper/1912.07011/) (2019)
- [Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds](https://mlsanity.com/paper/2003.04210/) (2020)
- [DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data](https://mlsanity.com/paper/2004.14079/) (2020)
- [Sound Localization by Self-Supervised Time Delay Estimation](https://mlsanity.com/paper/2204.12489/) (2022)
- [Structure from Silence: Learning Scene Structure from Ambient Sound](https://mlsanity.com/paper/2111.05846/) (2021)
- [Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring](https://mlsanity.com/paper/1903.00650/) (2019)
- [Multisensor Online Transfer Learning for 3D LiDAR-based Human Detection with a Mobile Robot](https://mlsanity.com/paper/1801.04137/) (2018)
- [2D vs. 3D LiDAR-based Person Detection on Mobile Robots](https://mlsanity.com/paper/2106.11239/) (2021)
- [Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations](https://mlsanity.com/paper/2203.00187/) (2022)
- [Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study](https://mlsanity.com/paper/2108.01495/) (2021)
- [Occlusion-Aware Object Localization, Segmentation and Pose Estimation](https://mlsanity.com/paper/1507.07882/) (2015)
- [Deep Person Detection in 2D Range Data](https://mlsanity.com/paper/1804.02463/) (2018)
- [Robust Sound Source Localization Using a Microphone Array on a Mobile Robot](https://mlsanity.com/paper/1602.08213/) (2016)
- [Realtime Active Sound Source Localization for Unmanned Ground Robots Using a Self-Rotational Bi-Microphone Array](https://mlsanity.com/paper/1804.03372/) (2018)

## Built on by (most cited first, 2 of 2)

- [Clink! Chop! Thud! -- Learning Object Sounds from Real-World Interactions](https://mlsanity.com/paper/2510.02313/) (2025)
- [NeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localization](https://mlsanity.com/paper/2606.18664/) (2026)

## 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