In this paper we discuss active humanoid vision systems that realize foveation using two rigidly connected cameras in each eye. We present an exhaustive analysis of the relationship between the positions of the observed point in the foveal and peripheral view with respect to the intrinsic and extrinsic parameters of both cameras and 3-D point position. Based on these results we propose a control scheme that can be used to maintain the view of the observed object in the foveal image using information from the peripheral view. Experimental results showing the effectiveness of the proposed foveation control are also provided

Object recognition requires a robot to perform a number of nontrivial tasks such as finding objects of interest, directing its eyes towards the objects, pursuing them, and identifying the objects once they appear in the robot's central vision. We have recently developed a recognition system on a humanoid robot, which makes use of foveated vision to accomplish these tasks (A Ude, et al., 2003). In this paper we present several substantial improvements to this system. We present a biologically motivated object representation scheme based on Gabor kernel functions and show how to employ support vector machines to identify known objects in foveal images based on this representation. A mechanism for visual search is integrated into the system to find objects of interest in peripheral images. The framework also includes a control scheme for eye movements, which are directed using the results of attentive processing in peripheral images.

Smooth control using an active vision head's verge-axis joint is performed through continuous state and action reinforcement learning. The system learns to perform visual servoing based on rewards given relative to tracking performance. The learned controller compensates for the velocity of the target and performs lag-free pursuit of a swinging target. By comparing controllers exposed to different environments we show that the controller is predicting the motion of the target by forming an implicit model of the target's motion. Experimental results are presented that demonstrate the advantages and disadvantages of implicit modelling.

Reinforcement learning systems improve behaviour based on scalar rewards from a critic. In this work vision based behaviours, servoing and wandering, are learned through a Q-learning method which handles continuous states and actions. There is no requirement for camera calibration, an actuator model, or a knowledgeable teacher. Learning through observing the actions of other behaviours improves learning speed. Experiments were performed on a mobile robot using a real-time vision system.