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Symbolic and Non-Symbolic Failure Interpretation and Recovery Using a Domestic Service Robot

Snijders, R (2016) Symbolic and Non-Symbolic Failure Interpretation and Recovery Using a Domestic Service Robot. Master's Thesis / Essay, Artificial Intelligence.

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Domestic service robots need to be robust against noise and a large degree of uncertainty. This also requires the ability to detect, recognize and resolve previously unknown failures during their lifetime. Existing research offers promising solutions, but typically depends on what was foreseen by its application. In this research we address this problem by the design, implementation and verification of an adaptive behavior architecture capable of autonomous failure recovery. The recovery performance of two different methods using a non-symbolic or a symbolic representation of the failure state respectively, is compared and evaluated. In addition, a method is proposed for the autonomous perception of symbols in the environment from low level sensory information. The non-symbolic approach uses low level sensory information (RGBD data retrieved from a color and depth camera) to estimate the current failure state the robot is in. A dissimilarity measure is used to select the k most similar failure situations. The number of k samples to use is dynamically determined by a sudden change in either the dissimilarity or the score distribution of the closest samples, whichever comes first. In its most basic form, the symbolic approach uses a Naive Bayes classifier to select the best recovery solution with the highest probability of being a success, given a set of symbols consisting out of concepts (nouns) and their properties (adjectives). In the extended form, the symbolic approach uses a set of transformed representations of the original symbolic representation, of which it is able to learn the best representation most suitable of a given failure situation. The symbol perception module uses a region growing algorithm to segment the pointcloud, as retrieved from a RGBD camera, into multiple surfaces. From each surface, a collection of features are extracted, such as its similarity to known 3D models using the MLESAC algorithm, a binned color histogram and metric information using PCA. After accumulation of labelled training samples, a template is created to which an unclassified segmented pointcloud can be matched to. Each feature is weighted by estimating the inverse overlap of the probability density function of one class to all other classes prior to finding the most prominent prototype vectors of a given class during template creation. During classification, a concept is added to the symbolic representation if a sufficient number of segmented surfaces have been matched to the corresponding concept class template. Once a concept is added to the symbolic representation, its properties are classified using kNN. Without knowing the different types or the total number of failure situations, both the non-symbolic and symbolic approach of failure recovery are able to learn recovery solutions at an adequate level. Using the symbolic representation yields the best recovery performance while being robust against misclassifications in the perception of the symbols. The symbolic approach is capable of learning the best simplification of the original representation, thereby increasing its performance while using this new representation to provide suggestive information as to why the failure occurred.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 08:11
Last Modified: 15 Feb 2018 08:11

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