Grounded Object Individuation by a Humanoid Robot

This paper proposes a theoretical model that enables a robot to partition its unlabeled sensorimotor experience with different objects into discrete clusters, each corresponding to a specific object. To solve this object individuation problem, the robot was trained to detect whether two perceptual stimuli were produced by the same object or by two different objects. The model was tested using a large-scale experiment in which a humanoid robot explored 100 different objects by performing a variety of exploratory behaviors on them and detecting the resulting sensory feedback from several sensory modalities. The results show that with a small amount of prior training, the robot's model was able to successfully individuate the objects with a high degree of accuracy.