@article {70, title = {Infomax Control of Eye Movements}, journal = {IEEE Transactions on Autonomous Mental Development}, volume = {2}, year = {2010}, pages = {91-107}, chapter = {91}, abstract = {

Recently, infomax methods of optimal control have begun to reshape how we think about active information gathering. We show how such methods can be used to formulate the problem of choosing where to look. We show how an optimal eye movement controller can be learned from subjective experiences of information gathering, and we explore in simulation properties of the optimal controller. This controller outperforms other eye movement strategies proposed in the literature. The learned eye movement strategies are tailored to the specific visual system of the learner-we show that agents with different kinds of eyes should follow different eye movement strategies. Then we use these insights to build an autonomous computer program that follows this approach and learns to search for faces in images faster than current state-of-the-art techniques. The context of these results is search in static scenes, but the approach extends easily, and gives further efficiency gains, to dynamic tracking tasks. A limitation of infomax methods is that they require probabilistic models of uncertainty of the sensory system, the motor system, and the external world. In the final section of this paper, we propose future avenues of research by which autonomous physical agents may use developmental experience to subjectively characterize the uncertainties they face.

}, keywords = {active information gathering, autonomous computer program, autonomous physical agent, Computer vision, dynamic tracking task, Eye movement, eye movement strategy, face detection, faces, Infomax control, motor system, object detection, optimal control, optimal eye movement controller, policy gradient, probabilistic model, sensory system, static scenes, Visual Perception, visual search, visual system}, issn = {1943-0604}, author = {Butko, N. and Movellan, J.} } @conference {64, title = {Learning to Make Facial Expressions}, booktitle = {IEEE 8th International Conference on Development and Learning, 2009. ICDL 2009}, year = {2009}, month = {06/2009}, publisher = {IEEE}, organization = {IEEE}, address = {Shanghai}, abstract = {

This paper explores the process of self-guided learning of realistic facial expression production by a robotic head with 31 degrees of freedom. Facial motor parameters were learned using feedback from real-time facial expression recognition from video. The experiments show that the mapping of servos to expressions was learned in under one-hour of training time. We discuss how our work may help illuminate the computational study of how infants learn to make facial expressions.

}, keywords = {Actuators, Emotion recognition, face detection, face recognition, facial motor parameters, Feedback, Humans, learning (artificial intelligence), Machine Learning, Magnetic heads, Pediatrics, real-time facial expression recognition, Robot sensing systems, robotic head, Robots, self-guided learning, Servomechanisms, Servomotors}, isbn = {978-1-4244-4117-4}, author = {Wu, T. and Butko, N. and Ruvulo, P. and Bartlett, M. and Movellan, J.} }