A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
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
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
Learning Bayesian networks: a unification for discrete and Gaussian domains
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
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Hidden Markov models (HMMS) and partially observable Markov decision processes (POMDPS)form a useful tool for modeling dynamical systems. They are particularly useful for representing environments such as road networks and office buildings, which are typical for robot navigation and planning. The work presented here is concerned with acquiring such models. We demonstrate how domain-specific information and consaaints can be incorporated into the statistical estimation process, greatly improving the learned models in terms of the model quality, the number of iterations required for convergence and robustness to reduction in the amount of available data. We present new initialization heuristics which can be used even when the data suffers from cumulative rotational error, new update rules for the model parameters, as an instance of generalized EM, and a strategy for enforcing complete geometrical consistency in the model. Experimental results demonstrate the effectiveness of our approach for both simulated and real robot data, in traditionally hard-to-learn environments.