A Bayesian Approach to Conceptualization and Place Classification: Incorporating Spatial Relationships (distances) to Infer Concepts

The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim, the presented work is part of an attempt to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, this work details efforts taken towards learning and generating concepts and attempts to classify places using the concepts gleaned. Inference is based on the number of occurrences of various objects. The approach is based on learning from exemplars, clustering and the use of Bayesian network classifiers. Such a conceptualization and the representation that results thereof, will enable robots to be more cognizant of their surroundings and yet, compatible to us. Experiments on conceptualization and place classification are reported. Thus, the theme of the work is conceptualization and classification for representation and spatial cognition.