REAL-WORLD ROBOTICS - LEARNING TO PLAN FOR ROBUST EXECUTION

In executing classical plans in the real world, small discrepancies be tween a planner's internal representations and the real world are unav oidable. These can conspire to cause real-world failures even though t he planner is sound and, therefore, ''proves'' that a sequence of acti ons achieves the goal. Permissive planning, a machine learning extensi on to classical planning, is one response to this difficulty. This pap er describes the permissive planning approach and presents GRASPER, a permissive planning robotic system that learns to robustly pick up nov el objects.