Robust learning of probabilistic hybrid models

Advances in autonomy, in the fields of control, estimation, and diagnosis, have improved immensely, as seen by spacecraft that navigate toward pinpoint landings, or speech recognition enabled in hand-held devices. Arguably the most important step to controlling and improving a system, is to understand that system. For this reason, accurate models are essential for continued advancements in the field of autonomy. Hybrid stochastic models, such as JMLS and LPHA, allow for representational accuracy of a general scope of problems. The goal of this thesis is to develop a robust method for learning accurate hybrid models automatically from data. A robust method should learn a set of model parameters, but should also avoid convergence to locally optimal solutions that reduce accuracy, and should be less sensitive to sparse or poor quality observation data. These three goals are the focus of this thesis. We present the HML-LPHA algorithm that uses approximate EM for learning maximum likelihood model parameters of LPHA, given a sequence of control inputs {u}0T, and outputs, {y}T+I 1 We implement the algorithm in a scenario that simulates the mechanical wheel failure of the MER Spirit rover wheel and demonstrate empirical convergence of the algorithm. Local convergence is a limitation of many optimization approaches for multimodal functions, including EM. For model learning, this can mean a severe compromise in accuracy. We present the kMeans-EM algorithm, that iteratively learns the locations and shapes of explored local maxima of our model likelihood function, and focuses the search away from these areas of the solution space toward undiscovered maxima that are promising apriori. We find the kMeans-EM algorithm demonstrates iteratively increasing improvement over a Random Restarts method with respect to learning sets of model parameters with higher likelihood values, and reducing Euclidean distance to the true set of model parameters. Lastly, the AHML-LPHA algorithm is an active hybrid model learning approach that augments sparse, and/or very noisy training data, with limited queries of the discrete state.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Gil, Stephanie, Ph. D. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Brian Williams.

• Aeronautics and Astronautics.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/46562

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/46562