# Deep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot

arXiv: https://arxiv.org/abs/1807.01035 · HTML: https://mlsanity.com/paper/1807.01035/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information](https://mlsanity.com/paper/2607.26047/) (2026)

## Abstract (arXiv)

We present a novel approach for interactive auditory object analysis with a humanoid robot. The robot elicits sensory information by physically shaking visually indistinguishable plastic capsules. It gathers the resulting audio signals from microphones that are embedded into the robotic ears. A neural network architecture learns from these signals to analyze properties of the contents of the containers. Specifically, we evaluate the material classification and weight prediction accuracy and demonstrate that the framework is fairly robust to acoustic real-world noise.