Learning and Intelligent Systems: Learning Minimal Representations for Visual Navigation and Recognition

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This project is being funded through the Learning and Intelligent Systems (LIS) initiative. The project is concerned with the intelligence exhibited in interactions among sensory-motor activities and cognitive capacities such as reasoning, planning and learning, in both organisms and machines. Such interaction is regularly shown in the act of navigation, which is engaged in by humans and other animals from an early age, and seems almost effortless in normal circumstances thereafter. Whatever there is in navigation that is innate and whatever is learned, it is important to try to understand the interaction of the various cognitive, perceptual, and motor systems that are involved. The complexity of these interactions becomes clear in the development of mobile robots, such as the one recently deployed on Mars, not to mention the more autonomous ones planned for the future. It is still a major and imperfectly understood task to create programs that will coordinate sensors, keep an internal emapn of the area, and allow the robot to cross a space efficiently and without collisions with obstacles. An interdisciplinary approach is being taken in this research project, exploring human capabilities through experiments, developing models based on the experimental results and what is already known about human navigation, implementing these models in programs for robot control, then testing these programs in robotic navigation experiments for their efficacy and their reasonableness as models of human navigation. The goals are both to understand the phenomena in humans and machines and to develop robust algorithms to be used in mobile robots. This alliance of researchers studying psychophysics, cognition, computation, and robotics will lead to gains in knowledge across many disciplines and will enhance our understanding of spatial cognition and visual navigation in agents, both artificial and natural.