Understanding of Ambiguous Language Commands Using Probabilistic Models

Abstract

In recent years, home service robots, which assist human daily life, have been developed. However, the service robots do not distribute wider in the real environment because of the difficulty of understanding human language commands. For instance, when the robot receives the command "Put in the kitchen sink," it must grasp an appropriate object before moving to the kitchen sink. The robot is required to determine when it should perform a task from language commands. Furthermore, the robot is required to recognize the validity of language commands because humans sometimes make mistakes. This paper tackles these issues by employing probabilistic models. Our proposed model learns the relationship between robot observations (e.g., object image, the robot position) and verbal commands in an unsupervised manner. We evaluate the proposed method in the recognition tasks of tense and validity of verbal commands. The results reveal that our proposed model outperforms other machine learning methods.