Concept formation by robots using an infinite mixture of models

We propose a method for a robot to form various concepts. The robot uses its embodiment to obtain visual, auditory, and haptic information by grasping, shaking, and observing objects. At the same time, a user teaches the robot object features through speech. From these kinds of information, the robot can form object concepts. The information obtained by the robot is converted into Bag-of-Words representations, which are classified into categories. The proposed method is based on a stochastic model, and objects can be classified by estimating their parameters. We introduce the Chinese restaurant process into the multimodal hierarchical Dirichlet process. This model is an infinite mixture of models and enables the robot to form various concepts such as object type, color, and so on. Therefore, the robot can form not only object concepts but also various concepts that are not represented by object concepts (e.g., color). Also, because the proposed method is based on a stochastic model, it makes it possible for the robot to estimate category and unobserved information of unseen objects. We implement the proposed method on a robot and show that it can form various concepts and perform various estimations for unseen objects.