The goal of robotics research is to design a robot to fulfill a variety of tasks in the real world. Inherent in the real world is a high degree of uncertainty about the robot’s behavior and about the world. We introduce a robot task architecture, DTRC, that generates plans with actions that incorporate costs and uncertain effects, and states that yield rewards. The use of a decision-theoretic planner in a robot task architecture is demonstrated on the mobile robot domain of miniature golf. The miniature golf domain shows the application of decision-theoretic planning in an inherently uncertain domain, and demonstrates that by using decision-theoretic planning as the reasoning method in a robot task architecture, accommodation for uncertain information plays a direct role in the reasoning process.
['Gilbert L. Peterson', 'Diane J. Cook']
Incorporating decision-theoretic planning in a robot architecture