Active Perception for Multimodal Object Category Recognition Using Information Gain

In this paper, we propose an optimal active perception method for recognizing multimodal object categories. Multimodal categorization methods enable a robot to form several multimodal categories through interaction with daily objects autonomously. In most settings, the robot has to obtain all of the modality information when it attempts to recognize a new target object. However, even though a robot obtains visual information at a distance, it cannot obtain haptic and auditory information without taking action on the object. The robot has to determine its next action to obtain information about the object to recognize it. We propose an action selection method for multimodal object category recognition on the basis of the multimodal hierarchical Dirichlet process (MHDP) and information gain criterion. We also prove its optimality from the viewpoint of the Kullback--Leibler divergence between a final recognition state and a current recognition state. In addition, we show that the information gain has submodularity owing to the graphical model of the MHDP. On the basis of the submodular property of the information gain criterion, we propose sequential action selection methods, a greedy algorithm, and a lazy greedy algorithm. We conduct an experiment using an upper-torso humanoid robot and show that the method enables the robot to select actions actively and recognize target objects efficiently.