Qualitative navigation for mobile robots
Artificial Intelligence
Spatial representation for navigation in animats
Adaptive Behavior
Reinforcement learning for landmark-based robot navigation
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Building portable options: skill transfer in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Generalization and transfer learning in noise-affected robot navigation tasks
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
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Navigation based on detected landmarks is an important facet of robot navigation. This work investigates into a qualitative representation of landmarks for an autonomous learning task where a robot learns a goal directed navigation strategy with reinforcement learning. We discuss how to build a suitable landmark-based representation. In particular, we focus on selection of landmarks to regard when experiencing a multitude of landmarks, because representing all of them would blow up the state space inappropriately. Thus, we examine strategies for this selection. Furthermore, we introduce a background knowledge based structure-aware landmark selection mechanism to limit landmark observation to the cases where it is really needed.