This paper describes an approach to interactive object categorization that couples exploratory behaviors and their resulting acoustic signatures to form object categories. The framework was tested with an upper-torso humanoid robot on a containeron-container categorization task. The robot used six exploratory behaviors (drop block, grasp, move, shake, flip, and drop object) and applied them to twenty objects. The results from this large-scale experimental study show that the robot was able to learn meaningful object categories using only acoustic information. The results also show that the quality of the categorization depends on the exploratory behavior used to derive it as some behaviors elicit more salient acoustic signatures than others.
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