Learning regular sets from queries and counterexamples
Information and Computation
Inference of finite automata using homing sequences
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
On the Computational Complexity of Approximating Distributions by Probabilistic Automata
Machine Learning - Computational learning theory
Learning probabilistic automata with variable memory length
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Learning dynamics: system identification for perceptually challenged agents
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
The nature of statistical learning theory
The nature of statistical learning theory
On the learnability and usage of acyclic probabilistic finite automata
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Map learning with uninterpreted sensors and effectors
Artificial Intelligence
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
On the learnability and usage of acyclic probabilistic finite automata
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Integrating topological and metroc maps for mobile robot navigation: a statistical approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Heading in the Right Direction
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning models for robot navigation
Learning models for robot navigation
Diversity-based inference of finite automata
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
Learning topological maps with weak local odometric information
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Integrating grid-based and topological maps for mobile robot navigation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Robot introspection through learned hidden Markov models
Artificial Intelligence
Finding approximate POMDP solutions through belief compression
Journal of Artificial Intelligence Research
Robot introspection through learned hidden Markov models
Artificial Intelligence
Journal of Intelligent and Robotic Systems
Map-based navigation in mobile robots
Cognitive Systems Research
Map-based navigation in mobile robots
Cognitive Systems Research
Robust offline topological map estimation using visual loop closures
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
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Hidden Markov models (HMMs) and partially observable Markov decision processes (POMDPs) provide useful tools for modeling dynamical systems. They are particularly useful for representing the topology of environments such as road networks and office buildings, which are typical for robot navigation and planning. The work presented here describes a formal framework for incorporating readily available odometric information and geometrical constraints into both the models and the algorithm that learns them. By taking advantage of such information, learning HMMs/POMDPs can be made to generate better solutions and require fewer iterations, while being robust in the face of data reduction. Experimental results, obtained from both simulated and real robot data, demonstrate the effectiveness of the approach.