Inferring robot goals from violations of semantic knowledge

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 present a novel use of semantic knowledge, to encode information about how things should be, i.e. norms, and to enable the robot to infer deviations from these norms in order 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. The key move is to properly encode norms in an ontology so that each norm violation results in a detectable inconsistency. A goal is then generated to bring the world back in a consistent state, and a planner is used to transform this goal into actions. 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, and standard perception and navigation routines.

► Represent normative constraints as part of the robot’s ontology.
► Automatically detect violations of these constraints.
► Automatically generate planning goals to recover from these violations.
► Multiple concurrent norm violations can be managed.
► Proof-of-concept experiments shown on the full robotic system.