Abstract
We argue that many problems in robotics arise from the difficulty of integrating multiple knowledge representation and inference techniques. We describe an architecture that integrates disparate reasoning, planning, sensation and mobility algorithms by composing them from strategies for managing mental simulations. Since simulations are conducted by modules that include high-level knowledge representation and inference techniques in addition to algorithms for sensation and reactive mobility, cognition, perception and action are continually integrated. An implemented robot using this framework in object-tacking and human–robot interaction tasks demonstrates that knowledge representation and inference techniques enable more complex and flexible robot behavior.
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