Learning of subgoals for goal-oriented behavior control of mobile robots

Abstract

Subgoal learning is investigated to effectively build a goal-oriented behavior control rule with which a mobile robot can achieve a task goal for any starting task configurations. For this, states of interest are firstly extracted from successful task episodes, where the averaged occurrence frequency of states is used as threshold value to identify states of interest. And, subgoals are learned by clustering similar features of state transition tuples. Here, features used in clustering are produced by using changes of the states in the state transition tuples. A goal-oriented behavior control rule is made in such a way that proper actions are sequentially and/or reactively generated from the subgoal according to the context of states. To show the validities of our proposed subgoal learning as well as a goal-oriented control rule of mobile robots, a Box-Pushing-Into-a-Goal(BPIG) task is simulated and experimented. © 2009 Springer Berlin Heidelberg.