Engineering evolutionary control for real-world robotic systems

Evolutionary Robotics (ER) is the field of study concerned with the application of evolutionary computation to the design of robotic systems. Two main issues have prevented ER from being applied to real-world tasks, namely scaling to complex tasks and the transfer of control to real-robot systems. Finding solutions to complex tasks is challenging for evolutionary approaches due to the bootstrap problem and deception. When the task goal is too difficult, the evolutionary process will drift in regions of the search space with equally low levels of performance and therefore fail to bootstrap. Furthermore, the search space tends to get rugged (deceptive) as task complexity increases, which can lead to premature convergence. Another prominent issue in ER is the reality gap. Behavioral control is typically evolved in simulation and then only transferred to the real robotic hardware when a good solution has been found. Since simulation is an abstraction of the real world, the accuracy of the robot model and its interactions with the environment is limited. As a result, control evolved in a simulator tends to display a lower performance in reality than in simulation. In this thesis, we present a hierarchical control synthesis approach that enables the use of ER techniques for complex tasks in real robotic hardware by mitigating the bootstrap problem, deception, and the reality gap. We recursively decompose a task into sub-tasks, and synthesize control for each sub-task. The individual behaviors are then composed hierarchically. The possibility of incrementally transferring control as the controller is composed allows transferability issues to be addressed locally in the controller hierarchy. Our approach features hybridity, allowing different control synthesis techniques to be combined. We demonstrate our approach in a series of tasks that go beyond the complexity of tasks where ER has been successfully applied. We further show that hierarchical control can be applied in single-robot systems and in multirobot systems. Given our long-term goal of enabling the application of ER techniques to real-world tasks, we systematically validate our approach in real robotic hardware. For one of the demonstrations in this thesis, we have designed and built a swarm robotic platform, and we show the first successful transfer of evolved and hierarchical control to a swarm of robots outside of controlled laboratory conditions.A Robótica Evolutiva (RE) é a área de investigação que estuda a aplicação de computação evolutiva na conceção de sistemas robóticos. Dois principais desafios têm impedido a aplicação da RE em tarefas do mundo real: a dificuldade em solucionar tarefas complexas e a transferência de controladores evoluídos para sistemas robóticos reais. Encontrar soluções para tarefas complexas é desafiante para as técnicas evolutivas devido ao bootstrap problem e à deception. Quando o objetivo é demasiado difícil, o processo evolutivo tende a permanecer em regiões do espaço de procura com níveis de desempenho igualmente baixos, e consequentemente não consegue inicializar. Por outro lado, o espaço de procura tende a enrugar à medida que a complexidade da tarefa aumenta, o que pode resultar numa convergência prematura. Outro desafio na RE é a reality gap. O controlo robótico é tipicamente evoluído em simulação, e só é transferido para o sistema robótico real quando uma boa solução tiver sido encontrada. Como a simulação é uma abstração da realidade, a precisão do modelo do robô e das suas interações com o ambiente é limitada, podendo resultar em controladores com um menor desempenho no mundo real. Nesta tese, apresentamos uma abordagem de síntese de controlo hierárquica que permite o uso de técnicas de RE em tarefas complexas com hardware robótico real, mitigando o bootstrap problem, a deception e a reality gap. Decompomos recursivamente uma tarefa em sub-tarefas, e sintetizamos controlo para cada subtarefa. Os comportamentos individuais são então compostos hierarquicamente. A possibilidade de transferir o controlo incrementalmente à medida que o controlador é composto permite que problemas de transferibilidade possam ser endereçados localmente na hierarquia do controlador. A nossa abordagem permite o uso de diferentes técnicas de síntese de controlo, resultando em controladores híbridos. Demonstramos a nossa abordagem em várias tarefas que vão para além da complexidade das tarefas onde a RE foi aplicada. Também mostramos que o controlo hierárquico pode ser aplicado em sistemas de um robô ou sistemas multirobô. Dado o nosso objetivo de longo prazo de permitir o uso de técnicas de RE em tarefas no mundo real, concebemos e desenvolvemos uma plataforma de robótica de enxame, e mostramos a primeira transferência de controlo evoluído e hierárquico para um exame de robôs fora de condições controladas de laboratório.This work has been supported by the Portuguese Foundation for Science and Technology (Fundação para a Ciência e Tecnologia) under the grants SFRH/BD/76438/2011, EXPL/EEI-AUT/0329/2013, and by Instituto de Telecomunicações under the grant UID/EEA/50008/2013

Time series data mining: preprocessing, analysis, segmentation and prediction. Applications

Currently, the amount of data which is produced for any information system is increasing exponentially. This motivates the development of automatic techniques to process and mine these data correctly. Specifically, in this Thesis, we tackled these problems for time series data, that is, temporal data which is collected chronologically. This kind of data can be found in many fields of science, such as palaeoclimatology, hydrology, financial problems, etc. TSDM consists of several tasks which try to achieve different objectives, such as, classification, segmentation, clustering, prediction, analysis, etc. However, in this Thesis, we focus on time series preprocessing, segmentation and prediction. Time series preprocessing is a prerequisite for other posterior tasks: for example, the reconstruction of missing values in incomplete parts of time series can be essential for clustering them. In this Thesis, we tackled the problem of massive missing data reconstruction in SWH time series from the Gulf of Alaska. It is very common that buoys stop working for different periods, what it is usually related to malfunctioning or bad weather conditions. The relation of the time series of each buoy is analysed and exploited to reconstruct the whole missing time series. In this context, EANNs with PUs are trained, showing that the resulting models are simple and able to recover these values with high precision. In the case of time series segmentation, the procedure consists in dividing the time series into different subsequences to achieve different purposes. This segmentation can be done trying to find useful patterns in the time series. In this Thesis, we have developed novel bioinspired algorithms in this context. For instance, for paleoclimate data, an initial genetic algorithm was proposed to discover early warning signals of TPs, whose detection was supported by expert opinions. However, given that the expert had to individually evaluate every solution given by the algorithm, the evaluation of the results was very tedious. This led to an improvement in the body of the GA to evaluate the procedure automatically. For significant wave height time series, the objective was the detection of groups which contains extreme waves, i.e. those which are relatively large with respect other waves close in time. The main motivation is to design alert systems. This was done using an HA, where an LS process was included by using a likelihood-based segmentation, assuming that the points follow a beta distribution. Finally, the analysis of similarities in different periods of European stock markets was also tackled with the aim of evaluating the influence of different markets in Europe. When segmenting time series with the aim of reducing the number of points, different techniques have been proposed. However, it is an open challenge given the difficulty to operate with large amounts of data in different applications. In this work, we propose a novel statistically-driven CRO algorithm (SCRO), which automatically adapts its parameters during the evolution, taking into account the statistical distribution of the population fitness. This algorithm improves the state-of-the-art with respect to accuracy and robustness. Also, this problem has been tackled using an improvement of the BBPSO algorithm, which includes a dynamical update of the cognitive and social components in the evolution, combined with mathematical tricks to obtain the fitness of the solutions, which significantly reduces the computational cost of previously proposed coral reef methods. Also, the optimisation of both objectives (clustering quality and approximation quality), which are in conflict, could be an interesting open challenge, which will be tackled in this Thesis. For that, an MOEA for time series segmentation is developed, improving the clustering quality of the solutions and their approximation. The prediction in time series is the estimation of future values by observing and studying the previous ones. In this context, we solve this task by applying prediction over high-order representations of the elements of the time series, i.e. the segments obtained by time series segmentation. This is applied to two challenging problems, i.e. the prediction of extreme wave height and fog prediction. On the one hand, the number of extreme values in SWH time series is less with respect to the number of standard values. In this way, the prediction of these values cannot be done using standard algorithms without taking into account the imbalanced ratio of the dataset. For that, an algorithm that automatically finds the set of segments and then applies EANNs is developed, showing the high ability of the algorithm to detect and predict these special events. On the other hand, fog prediction is affected by the same problem, that is, the number of fog events is much lower tan that of non-fog events, requiring a special treatment too. A preprocessing of different data coming from sensors situated in different parts of the Valladolid airport are used for making a simple ANN model, which is physically corroborated and discussed. The last challenge which opens new horizons is the estimation of the statistical distribution of time series to guide different methodologies. For this, the estimation of a mixed distribution for SWH time series is then used for fixing the threshold of POT approaches. Also, the determination of the fittest distribution for the time series is used for discretising it and making a prediction which treats the problem as ordinal classification. The work developed in this Thesis is supported by twelve papers in international journals, seven papers in international conferences, and four papers in national conferences

Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations

Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary Algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimisation and training of DNNs. Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Prolonged absence of such surveys can lead to a disjointed and fragmented field preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for improving performance and wider application within real-world deep learning problems. This paper presents a comprehensive survey, discussion and evaluation of the state-of-the-art works on using EAs for architectural configuration and training of DNNs. Based on this survey, the paper highlights the most pertinent current issues and challenges in neuroevolution and identifies multiple promising future research directions.Comment: 20 pages (double column), 2 figures, 3 tables, 157 reference

Anomaly Detection Using Hierarchical Temporal Memory in Smart Homes

This work focuses on unsupervised biologically-inspired machine learning techniques and algorithms that can detect anomalies. Specifically, the aim is to investigate the applicability of the Hierarchical Temporal Memory (HTM) theory in detecting anomalies in the smart home domain. The HTM theory proposes a model for the neurons that is more faithful to the actual neurons than their usual counterparts in Artificial Neural Networks (ANN) based on the current Neuroscience understanding. The HTM theory has several algorithmic implementations, the most prominent one is the Cortical Learning Algorithm (CLA). The CLA model typically consists of three main regions: the encoder, the spatial pooler and the temporal memory. Studying the performance of the CLA in the smart home domain revealed an issue with the standard encoders and high-dimensional datasets. In this domain, it is typical to have high-dimensional feature space representing the collection of smart devices. The standard CLA encoders are more suitable for low-dimensional datasets and there are encoders for categorical and scalar data types. A novel Hash Indexed Sparse Distributed Representation (HI-SDR) encoder was proposed and developed, to overcome the high-dimensionality issue. The HI-SDR encoder creates unique representation of the data which allows the rest of the CLA regions to learn from. The standard approach when creating HTM models to work with datasets with many features is to concatenate the output of each encoder. This work concludes that the standard encoders produced representations for the input during every timestep that were similar and less distinguishable for the HTM model. This output similarity confuses the HTM model and makes it hard to discern meaningful representations. The proposed novel encoder manages to capture the required properties in terms of sparsity and representations. To investigate and validate the performance of a proposed machine learning technique, there has to be a representative dataset. In the smart home literature, there exists many real-world smart home datasets that allow the researchers to validate their models. However, most of the existing datasets are created for classification and recognition of Activities of Daily Living (ADL). The lack of datasets for anomaly detection applications in the domain of smart homes required the development of a simulation tool. OpenSHS (Open Smart Home Simulator) was developed as an open-source, 3D and cross-platform smart home simulator that offers a novel hybrid approach to dataset generation. The tool allows the researchers to design a smart home and populate it with the needed smart devices. Then, the participants can use the designed smart home and simulate their habits and patterns. Anomaly detection in the smart home domain is highly contextual and dependent on the inhabitant’s activities. One inhabitant’s anomaly could be the norm for another, therefore the definition of anomalies is a complex consideration. Using OpenSHS, seven participants were invited to generated forty-two datasets of their activities. Moreover, each participant defined his/her own anomalous pattern that he/she would like the model to detect. Thus, the resulting datasets are annotated with contextual anomalies specific to each participant. The proposed encoder has been evaluated and compared against the standard CLA encoders and several state-of-the-art unsupervised anomaly detection algorithms, using Numenta Anomaly Benchmark (NAB). The HI-SDR encoder scored 81.9% accuracy, on the forty-two datasets, with 17.8% increase in accuracy compared to the k-NN algorithm and 47.5% increase over the standard CLA encoders. Using the Principal Component Analysis (PCA) algorithm as a preprocessing step proved to be beneficial to some of the tested algorithms. The k-NN algorithm scored 39.9% accuracy without PCA and scored 64.1% accuracy with PCA. Similarly, the Histogram Based Outlier Score (HBOS) algorithm scored 28.5% accuracy without PCA and 61.9% with PCA. The HTM-based models empirically showed good potential and exceeded in performance several algorithms, even without the HI-SDR encoder. However, the HTM-based models still lack an optimisation algorithm for its parameters when performing anomaly detection