The complexity of the optimal searcher path problem
Operations Research
A maximum entropy approach to natural language processing
Computational Linguistics
AI Magazine
Machine Learning
A frontier-based approach for autonomous exploration
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Simplifying decision trees: A survey
The Knowledge Engineering Review
Conceptual spatial representations for indoor mobile robots
Robotics and Autonomous Systems
Bayesian space conceptualization and place classification for semantic maps in mobile robotics
Robotics and Autonomous Systems
Classifier-Based Policy Representation
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Modeling RFID signal strength and tag detection for localization and mapping
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Utilizing object-object and object-scene context when planning to find things
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
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We consider the problem of efficiently finding an object with a mobile robot in an initially unknown, structured environment. The overall goal is to allow the robot to improve upon a standard exploration technique by utilizing background knowledge from previously seen, similar environments. We present two conceptually different approaches. Whereas the first method, which is the focus of this article, is a reactive search technique that decides where to search next only based on local information about the objects in the robot's vicinity, the second algorithm is a more global and inference-based approach that explicitly reasons about the location of the target object given all observations made so far. While the model underlying the first approach can be learned from data of optimal search paths, we learn the model of the second method from object arrangements of example environments. Our application scenario is the search for a product in a supermarket. We present simulation and real-world experiments in which we compare our strategies to alternative methods and also to the performance of humans.