Learning Adaptive Sensorimotor Representations

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A core competency of any intelligent physical system is its ability to operate successfully in a wide range of environments and to adapt autonomously to changes in task conditions. Current state-of-the-art systems for robot perception and control are increasingly able to overcome challenging environmental factors, typically through the use of "big-data" and machine learning techniques, but these systems require extensive human engineering and data annotation and are typically not flexible enough to overcome task variations without hands-on interaction from their human designer.