Abstract
This paper discusses the integration of machine learning and sensor-based control in intelligent robotic systems. Our research is interdisciplinary and combines techniques of explanation-based control with robust and adaptive nonlinear control, computer vision, and motion planning. Our intent in this research is to go beyond the strict hierarchical control architectures typically used in robotic systems by integrating modeling, dynamics, and control across traditional levels of planning and control at all levels of intelligence. Our ultimate goal is to combine analytical techniques of nonlinear dynamics and control with artificial intelligence into a single new paradigm in which symbolic reasoning holds an equal place with differential equation based modeling and control.
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