Real-World Robotics: Learning to Plan for Robust Execution |
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Authors: | Bennett Scott W DeJong Gerald F |
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Affiliation: | (1) Systems Research and Applications Corporation, 2000 15th Street North, Arlington, VA 22201, USA;(2) Beckman Institute, University of Illinois, 405 North Mathews Avenue, Urbana, IL 61801, USA |
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Abstract: | In executing classical plans in the real world, small discrepancies between a planner's internal representations and the real world are unavoidable. These can conspire to cause real-world failures even though the planner is sound and, therefore, proves that a sequence of actions achieves the goal. Permissive planning, a machine learning extension to classical planning, is one response to this difficulty. This paper describes the permissive planning approach and presents GRASPER, a permissive planning robotic system that learns to robustly pick up novel objects. |
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Keywords: | machine learning robotics uncertainty planning |
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