Multimodal object categorization by a robot

In this paper unsupervised object categorization by robots is examined. We propose an unsupervised multimodal categorization based on audio-visual and haptic information. The robot uses its physical embodiment to grasp and observe an object from various view points as well as listen to the sound during the observation. The proposed categorization method is an extension of probabilistic latent semantic analysis(pLSA), which is a statistical technique. At the same time the proposed method provides a probabilistic framework for inferring the object property from limited observations. Validity of the proposed method is shown through some experimental results.