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.
VL - 2 IS - 2 ER - TY - CONF T1 - A barebones communicative robot based on social contingency and Infomax Control T2 - The 17th IEEE International Symposium on Robot and Human Interactive Communication, 2008. RO-MAN 2008 Y1 - 2008 A1 - Tanaka, F. A1 - Movellan, J. KW - Actuators KW - barebones communicative robot KW - Communication system control KW - Delay KW - Detectors KW - Human robot interaction KW - human-model updating capability KW - humanoid robots KW - Hydrogen KW - Infomax control KW - man-machine systems KW - Pediatrics KW - policy improvement KW - Robot control KW - Robot sensing systems KW - Scheduling KW - social contingency AB -In this paper, we present a barebones robot which is capable of interacting with humans based on social contingency. It expands the previous work of a contingency detector into having both human-model updating (developmental capability) and policy improvement (learning capability) based on the framework of Infomax control. The proposed new controller interacts with humans in both active and responsive ways handling the turn-taking between them.
JF - The 17th IEEE International Symposium on Robot and Human Interactive Communication, 2008. RO-MAN 2008 PB - IEEE CY - Munich SN - 978-1-4244-2212-8 ER -