Deep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)
Contribution to Conference
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.