A visual attention network for a humanoid robot

Abstract

For a humanoid robot to interact easily with a person, the robot should have human-like sensory capabilities and attentional mechanisms. Particularly useful is an active vision head controlled by a visual attention system that selects viewpoints in the environment as a function of the robot's task. This paper describes a model of human visual attention called FeatureGate, which is a locally connected, pyramidal, artificial neural network that operates on 2D feature maps of the environment. Given a set of feature maps, and the description of a specific target, FeatureGate finds the location whose features most closely match those of the target. The paper describes the network, its implementation, a series of tests that characterize its performance with respect to a person's performance on a similar task, and its use in the control of an active vision system.