  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.
