Human-robot interaction and computer-vision-based services for autonomous robots

L'Aprenentatge per Imitació (IL), o Programació de robots per Demostració (PbD), abasta mètodes pels quals un robot aprèn noves habilitats a través de l'orientació humana i la imitació. La PbD s'inspira en la forma en què els éssers humans aprenen noves habilitats per imitació amb la finalitat de desenvolupar mètodes pels quals les noves tasques es poden transferir als robots. Aquesta tesi està motivada per la pregunta genèrica de "què imitar?", Que es refereix al problema de com extreure les característiques essencials d'una tasca. Amb aquesta finalitat, aquí adoptem la perspectiva del Reconeixement d'Accions (AR) per tal de permetre que el robot decideixi el què cal imitar o inferir en interactuar amb un ésser humà. L'enfoc proposat es basa en un mètode ben conegut que prové del processament del llenguatge natural: és a dir, la bossa de paraules (BoW). Aquest mètode s'aplica a grans bases de dades per tal d'obtenir un model entrenat. Encara que BoW és una tècnica d'aprenentatge de màquines que s'utilitza en diversos camps de la investigació, en la classificació d'accions per a l'aprenentatge en robots està lluny de ser acurada. D'altra banda, se centra en la classificació d'objectes i gestos en lloc d'accions. Per tant, en aquesta tesi es demostra que el mètode és adequat, en escenaris de classificació d'accions, per a la fusió d'informació de diferents fonts o de diferents assajos. Aquesta tesi fa tres contribucions: (1) es proposa un mètode general per fer front al reconeixement d'accions i per tant contribuir a l'aprenentatge per imitació; (2) la metodologia pot aplicar-se a grans bases de dades, que inclouen diferents modes de captura de les accions; i (3) el mètode s'aplica específicament en un projecte internacional d'innovació real anomenat Vinbot.El Aprendizaje por Imitación (IL), o Programación de robots por Demostración (PbD), abarca métodos por los cuales un robot aprende nuevas habilidades a través de la orientación humana y la imitación. La PbD se inspira en la forma en que los seres humanos aprenden nuevas habilidades por imitación con el fin de desarrollar métodos por los cuales las nuevas tareas se pueden transferir a los robots. Esta tesis está motivada por la pregunta genérica de "qué imitar?", que se refiere al problema de cómo extraer las características esenciales de una tarea. Con este fin, aquí adoptamos la perspectiva del Reconocimiento de Acciones (AR) con el fin de permitir que el robot decida lo que hay que imitar o inferir al interactuar con un ser humano. El enfoque propuesto se basa en un método bien conocido que proviene del procesamiento del lenguaje natural: es decir, la bolsa de palabras (BoW). Este método se aplica a grandes bases de datos con el fin de obtener un modelo entrenado. Aunque BoW es una técnica de aprendizaje de máquinas que se utiliza en diversos campos de la investigación, en la clasificación de acciones para el aprendizaje en robots está lejos de ser acurada. Además, se centra en la clasificación de objetos y gestos en lugar de acciones. Por lo tanto, en esta tesis se demuestra que el método es adecuado, en escenarios de clasificación de acciones, para la fusión de información de diferentes fuentes o de diferentes ensayos. Esta tesis hace tres contribuciones: (1) se propone un método general para hacer frente al reconocimiento de acciones y por lo tanto contribuir al aprendizaje por imitación; (2) la metodología puede aplicarse a grandes bases de datos, que incluyen diferentes modos de captura de las acciones; y (3) el método se aplica específicamente en un proyecto internacional de innovación real llamado Vinbot.Imitation Learning (IL), or robot Programming by Demonstration (PbD), covers methods by which a robot learns new skills through human guidance and imitation. PbD takes its inspiration from the way humans learn new skills by imitation in order to develop methods by which new tasks can be transmitted to robots. This thesis is motivated by the generic question of “what to imitate?” which concerns the problem of how to extract the essential features of a task. To this end, here we adopt Action Recognition (AR) perspective in order to allow the robot to decide what has to be imitated or inferred when interacting with a human kind. The proposed approach is based on a well-known method from natural language processing: namely, Bag of Words (BoW). This method is applied to large databases in order to obtain a trained model. Although BoW is a machine learning technique that is used in various fields of research, in action classification for robot learning it is far from accurate. Moreover, it focuses on the classification of objects and gestures rather than actions. Thus, in this thesis we show that the method is suitable in action classification scenarios for merging information from different sources or different trials. This thesis makes three contributions: (1) it proposes a general method for dealing with action recognition and thus to contribute to imitation learning; (2) the methodology can be applied to large databases which include different modes of action captures; and (3) the method is applied specifically in a real international innovation project called Vinbot

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihoodbased classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrencebased texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms

Probabilistic prediction of Alzheimer’s disease from multimodal image data with Gaussian processes

Alzheimer’s disease, the most common form of dementia, is an extremely serious health problem, and one that will become even more so in the coming decades as the global population ages. This has led to a massive effort to develop both new treatments for the condition and new methods of diagnosis; in fact the two are intimately linked as future treatments will depend on earlier diagnosis, which in turn requires the development of biomarkers that can be used to identify and track the disease. This is made possible by studies such as the Alzheimer’s disease neuroimaging initiative which provides previously unimaginable quantities of imaging and other data freely to researchers. It is the task of early diagnosis that this thesis focuses on. We do so by borrowing modern machine learning techniques, and applying them to image data. In particular, we use Gaussian processes (GPs), a previously neglected tool, and show they can be used in place of the more widely used support vector machine (SVM). As combinations of complementary biomarkers have been shown to be more useful than the biomarkers are individually, we go on to show GPs can also be applied to integrate different types of image and non-image data, and thanks to their properties this improves results further than it does with SVMs. In the final two chapters, we also look at different ways to formulate both the prediction of conversion to Alzheimer’s disease as a machine learning problem and the way image data can be used to generate features for input as a machine learning algorithm. Both of these show how unconventional approaches may improve results. The result is an advance in the state-of-the-art for a very clinically important problem, which may prove useful in practice and show a direction of future research to further increase the usefulness of such method

Imaging biomarkers extraction and classification for Prion disease

Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by inheritance of prion protein gene mutations or exposure to prions. To date, there are no accurate imaging biomarkers that can be used to predict the future diagnosis of a subject or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the large heterogeneity of phenotypes of prion disease and the lack of a consistent spatial pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of the human form of prion disease. Using a tailored framework, I extracted quantitative imaging biomarkers for characterisation of patients with Prion diseases. Following the extraction of patient-specific imaging biomarkers from multiple images, I implemented a Gaussian Process approach to correlated symptoms with disease types and stages. The model was used on three different tasks: diagnosis, differential diagnosis and stratification, addressing an unmet need to automatically identify patients with or at risk of developing Prion disease. The work presented in this thesis has been extensively validated in a unique Prion disease cohort, comprising both the inherited and sporadic forms of the disease. The model has shown to be effective in the prediction of this illness. Furthermore, this approach may have used in other disorders with heterogeneous imaging features, being an added value for the understanding of neurodegenerative diseases. Lastly, given the rarity of this disease, I also addressed the issue of missing data and the limitations raised by it. Overall, this work presents progress towards modelling of Prion diseases and which computational methodologies are potentially suitable for its characterisation

Approximation contexts in addressing graph data structures

While the application of machine learning algorithms to practical problems has been expanded from fixed sized input data to sequences, trees or graphs input data, the composition of learning system has developed from a single model to integrated ones. Recent advances in graph based learning algorithms include: the SOMSD (Self Organizing Map for Structured Data), PMGraphSOM (Probability Measure Graph Self Organizing Map,GNN (Graph Neural Network) and GLSVM (Graph Laplacian Support Vector Machine). A main motivation of this thesis is to investigate if such algorithms, whether by themselves individually or modified, or in various combinations, would provide better performance over the more traditional artificial neural networks or kernel machine methods on some practical challenging problems. More succinctly, this thesis seeks to answer the main research question: when or under what conditions/contexts could graph based models be adjusted and tailored to be most efficacious in terms of predictive or classification performance on some challenging practical problems? There emerges a range of sub-questions including: how do we craft an effective neural learning system which can be an integration of several graph and non-graph based models? Integration of various graph based and non graph based kernel machine algorithms; enhancing the capability of the integrated model in working with challenging problems; tackling the problem of long term dependency issues which aggravate the performance of layer-wise graph based neural systems. This thesis will answer these questions. Recent research on multiple staged learning models has demonstrated the efficacy of multiple layers of alternating unsupervised and supervised learning approaches. This underlies the very successful front-end feature extraction techniques in deep neural networks. However much exploration is still possible with the investigation of the number of layers required, and the types of unsupervised or supervised learning models which should be used. Such issues have not been considered so far, when the underlying input data structure is in the form of a graph. We will explore empirically the capabilities of models of increasing complexities, the combination of the unsupervised learning algorithms, SOM, or PMGraphSOM, with or without a cascade connection with a multilayer perceptron, and with or without being followed by multiple layers of GNN. Such studies explore the effects of including or ignoring context. A parallel study involving kernel machines with or without graph inputs has also been conducted empirically

Machine Learning Tools for Radio Map Estimation in Fading-Impaired Channels

In spectrum cartography, also known as radio map estimation, one constructs maps that provide the value of a given channel metric such as as the received power, power spectral density (PSD), electromagnetic absorption, or channel-gain for every spatial location in the geographic area of interest. The main idea is to deploy sensors and measure the target channel metric at a set of locations and interpolate or extrapolate the measurements. Radio maps nd a myriad of applications in wireless communications such as network planning, interference coordination, power control, spectrum management, resource allocation, handoff optimization, dynamic spectrum access, and cognitive radio. More recently, radio maps have been widely recognized as an enabling technology for unmanned aerial vehicle (UAV) communications because they allow autonomous UAVs to account for communication constraints when planning a mission. Additional use cases include radio tomography and source localization.publishedVersio

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page