A Behavior-Grounded Approach to Forming Object Categories: Separating Containers From Noncontainers

Abstract

This paper introduces a framework that allows a robot to form a single behavior-grounded object categorization after it uses multiple exploratory behaviors to interact with objects and multiple sensory modalities to detect the outcomes that each behavior produces. Our robot observed acoustic and visual outcomes from six different exploratory behaviors performed on 20 objects (containers and noncontainers). Its task was to learn 12 different object categorizations (one for each behavior-modality combination), and then to unify these categorizations into a single one. In the end, the object categorization acquired by the robot matched closely the object labels provided by a human. In addition, the robot acquired a visual model of containers and noncontainers based on its unified categorization, which it used to label correctly 29 out of 30 novel objects.