Robots that change their world: Inferring Goals from Semantic Knowledge (original) (raw)

A growing body of literature shows that endowing a mobile robot with semantic knowledge, and with the ability to reason from this knowledge, can greatly increase its capabilities. In this paper, we explore a novel use of semantic knowledge: we encode information about how things should be, or norms, to allow the robot to infer deviations from these norms and to generate goals to correct these deviations. For instance, if a robot has semantic knowledge that perishable items must be kept in a refrigerator, and it observes a bottle of milk on a table, this robot will generate the goal to bring that bottle into a refrigerator. Our approach provides a mobile robot with a limited form of goal autonomy: the ability to derive its own goals to pursue generic aims. We illustrate our approach in a full mobile robot system that integrates a semantic map, a knowledge representation and reasoning system, a task planner, as well as standard perception and navigation routines.