Digging through the dirt: a general method for abstract discrete state estimation with limited prior knowledge

Autonomous robots are often successfully deployed in controlled environments. Operation in uncontrolled situations remains challenging; it is hypothesized that the detection of abstract discrete states (ADS) can improve operation in these circumstances. ADS are high-level system states that are not directly detectable and influence system dynamics. An example of a typical ADS problem that is used in this thesis is that of a wheeled robot driving through puddles of mud that, when entered, alters the velocity of the robot. When the robot is in such a puddle, it is in an ADS 'mud', and when it is not, it is in an ADS 'free'. ADS can be indirectly inferred through the analysis of lower-level data such as the velocity of the robot. The goal of this thesis is to design a general abstract discrete state estimator (ADSE) operating with limited prior knowledge. An ADSE is a hierarchical system for detecting changes in ADS. The ADSE should be general; applicable to multiple ADSE problems. The ADSE should further operate under limited prior knowledge: only assuming that the amount of ADS and the ADS that describes the regular operation are known. The basis for the ADSE designed in this thesis is a Gaussian hidden Markov model (GHMM), a hidden Markov model enhanced with Gaussian emissions. Randomly generated experiments are done on a simple but general ADSE problem. Two unsupervised learning methods derived from Expectation Maximization are evaluated, namely Baum-Welch (BW) and forward extraction (FWE). FWE is introduced in this thesis and is a simpler implementation of Viterbi extraction, leveraging assumptions of ADSE to in theory gain computational efficiency. We found that both BW and FWE exhibit superior performance compared to a likelihood-based baseline estimator when the maximum score of the learning curve is considered. When the final score is considered, in some cases, FWE displays a deteriorating learning curve, resulting in worse final scores com
https://github.com/Wouter-deBoer/adse
Mechanical Engineering | Vehicle Engineering | Cognitive Robotics