Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

Students' collective knowledge construction in the virtual learning environment ""ToLigado - your school interactive newspaper""

Introduction. The ToLigado Project - Your School Interactive Newspaper is an interactive virtual learning environment conceived, developed, implemented and supported by researchers at the School of the Future Research Laboratory of the University of Sao Paulo, Brazil. Method. This virtual learning environment aims to motivate trans-disciplinary research among public school students and teachers in 2,931 schools equipped with Internet-access computer rooms. Within this virtual community, students produce collective multimedia research documents that are immediately published in the portal. The project also aims to increase students' autonomy for research, collaborative work and Web authorship. Main sections of the portal are presented and described. Results. Partial results of the first two years' implementation are presented and indicate a strong motivation among students to produce knowledge despite the fragile hardware and software infrastructure at the time. Discussion. In this new environment, students should be seen as 'knowledge architects' and teachers as facilitators, or 'curiosity managers'. The ToLigado portal may constitute a repository for future studies regarding student attitudes in virtual learning environments, students' behaviour as 'authors', Web authorship involving collective knowledge production, teachers' behaviour as facilitators, and virtual learning environments as digital repositories of students' knowledge construction and social capital in virtual learning communities.

Effects of postnatal protein malnutrition on learning and memory procedures

Protein malnutrition induces structural, neurochemical and functional changes in the central nervous system leading to alterations in cognitive and behavioral development of rats. The aim of this work was to investigate the effects of postnatal protein malnutrition on learning and memory tasks. Previously malnourished (6% protein) and well-nourished rats (16% protein) were tested in three experiments: working memory tasks in the Morris water maze (Experiment I), recognition memory of objects (Experiment II), and working memory in the water T-maze (Experiment III). The results showed higher escape latencies in malnourished animals in Experiment I, lower recognition indexes of malnourished animals in Experiment II, and no differences due to diet in Experiment III. It is suggested that protein malnutrition imposed on early life of rats can produce impairments on both working memory in the Morris maze and recognition memory in the open field tests.

Machine Learning Algorithms at Myocardial Perfusion Imaging - a Preliminary Study

Introduction: A major focus of data mining process - especially machine learning researches - is to automatically learn to recognize complex patterns and help to take the adequate decisions strictly based on the acquired data. Since imaging techniques like MPI – Myocardial Perfusion Imaging on Nuclear Cardiology, can implicate a huge part of the daily workflow and generate gigabytes of data, there could be advantages on Computerized Analysis of data over Human Analysis: shorter time, homogeneity and consistency, automatic recording of analysis results, relatively inexpensive, etc.Objectives: The aim of this study relates with the evaluation of the efficacy of this methodology on the evaluation of MPI Stress studies and the process of decision taking concerning the continuation – or not – of the evaluation of each patient. It has been pursued has an objective to automatically classify a patient test in one of three groups: “Positive”, “Negative” and “Indeterminate”. “Positive” would directly follow to the Rest test part of the exam, the “Negative” would be directly exempted from continuation and only the “Indeterminate” group would deserve the clinician analysis, so allowing economy of clinician’s effort, increasing workflow fluidity at the technologist’s level and probably sparing time to patients. Methods: WEKA v3.6.2 open source software was used to make a comparative analysis of three WEKA algorithms (“OneR”, “J48” and “Naïve Bayes”) - on a retrospective study using the comparison with correspondent clinical results as reference, signed by nuclear cardiologist experts - on “SPECT Heart Dataset”, available on University of California – Irvine, at the Machine Learning Repository. For evaluation purposes, criteria as “Precision”, “Incorrectly Classified Instances” and “Receiver Operating Characteristics (ROC) Areas” were considered. Results: The interpretation of the data suggests that the Naïve Bayes algorithm has the best performance among the three previously selected algorithms. Conclusions: It is believed - and apparently supported by the findings - that machine learning algorithms could significantly assist, at an intermediary level, on the analysis of scintigraphic data obtained on MPI, namely after Stress acquisition, so eventually increasing efficiency of the entire system and potentially easing both roles of Technologists and Nuclear Cardiologists. In the actual continuation of this study, it is planned to use more patient information and significantly increase the population under study, in order to allow improving system accuracy.

Modellus: Learning Physics with Mathematical Modelling

Tese de doutoramento em Ciências da Educação, área de Teoria Curricular e Ensino das Ciências

Learning with tangible interfaces

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Using device detection techniques in M-learning scenarios

Recent studies of mobile Web trends show the continued explosion of mobile-friend content. However, the wide number and heterogeneity of mobile devices poses several challenges for Web programmers, who want automatic delivery of context and adaptation of the content to mobile devices. Hence, the device detection phase assumes an important role in this process. In this chapter, the authors compare the most used approaches for mobile device detection. Based on this study, they present an architecture for detecting and delivering uniform m-Learning content to students in a Higher School. The authors focus mainly on the XML device capabilities repository and on the REST API Web Service for dealing with device data. In the former, the authors detail the respective capabilities schema and present a new caching approach. In the latter, they present an extension of the current API for dealing with it. Finally, the authors validate their approach by presenting the overall data and statistics collected through the Google Analytics service, in order to better understand the adherence to the mobile Web interface, its evolution over time, and the main weaknesses.

Haptic robot-environment interaction for self-supervised learning in ground mobility

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores

A behavioral study of alpha-1b adrenergic receptor knockout mice: increased reaction to novelty and selectively reduced learning capacities.

Knockout mice lacking the alpha-1b adrenergic receptor were tested in behavioral experiments. Reaction to novelty was first assessed in a simple test in which the time taken by the knockout mice and their littermate controls to enter a second compartment was compared. Then the mice were tested in an open field to which unknown objects were subsequently added. Special novelty was introduced by moving one of the familiar objects to another location in the open field. Spatial behavior and memory were further studied in a homing board test, and in the water maze. The alpha-1b knockout mice showed an enhanced reactivity to new situations. They were faster to enter the new environment, covered longer paths in the open field, and spent more time exploring the new objects. They reacted like controls to modification inducing spatial novelty. In the homing board test, both the knockout mice and the control mice seemed to use a combination of distant visual and proximal olfactory cues, showing place preference only if the two types of cues were redundant. In the water maze the alpha-1b knockout mice were unable to learn the task, which was confirmed in a probe trial without platform. They were perfectly able, however, to escape in a visible platform procedure. These results confirm previous findings showing that the noradrenergic pathway is important for the modulation of behaviors such as reaction to novelty and exploration, and suggest that this is mediated, at least partly, through the alpha-1b adrenergic receptors. The lack of alpha-1b adrenergic receptors in spatial orientation does not seem important in cue-rich tasks but may interfere with orientation in situations providing distant cues only.

How the mind constitutes itself through perceptual learning

Most theories of perception assume a rigid relationship between objects of the physical world and the corresponding mental representations. We show by a priori reasoning that this assumption is not fulfilled. We claim instead that all object-representation correspondences have to be learned. However, we cannot learn to perceive all objects that there are in the world. We arrive at these conclusions by a combinatory analysis of a fictive stimulus world and the way to cope with its complexity, which is perceptual learning. We show that successful perceptual learning requires changes in the representational states of the brain that are not derived directly from the constitution of the physical world. The mind constitutes itself through perceptual learning.

Structuring OER Using Learning Trajectories

The Wikiwijs program in the Netherlands is experimenting in structuring a repository with digital learning materials by labelling these materials with the learning goals and subjects handled by it. This makes it possible to create an interdependent arrangement of learning materials as building blocks for a curriculum. Such arrangements are called learning trajectories. A datamodel is presented in which the entities involved and their relationships are depicted. A first implementation of this is realized and published in September 2010.

HypatiaSalud. Institucional Repository for the Andalusian Public Health System, Spain

HypatiaSalud will be the freely accessible institutional repository for the Public Health System in Andalucía, Spain. Open access and new technologies have changed dramatically the environment in which research is being conducted and disseminated. Traditionally University has been the universal research provider, but at present time there are Government Organizations, as Public Health Systems, which are large-producers of research. Meanwhile most universities are running institutional repositories or have plans of setting up institutional repositories in the short-term, there are not many Government Organizations working on that direction. In this sense, HypatiaSalud represents an innovative initiative. Objectives: - Enhancing institutional efficiency, effectiveness and opportunities for knowledge exchange. -Expanding access and greater use of research findings to a much wider range of users increasing the visibility and reputation of Andalusian Public Health System. - Providing the foundation for effective gathering and long-term preservation of research output. Methodology. Phase I: Researching and learning from other institutional repositories. Phase II: Designing and planning the financial, organizational, legal and technical underlying issues. Phase III: Launching the service. Phase IV: Running the service. Outcomes. Bibliometrics: catalog of the research output of the Institution, in order to determine the conditions to include this scientific output in the Institutional Repository: direct deposit, deposit after a period of embargo, or closed access when publisher will not grant permission. Promote a mandate for the deposit of all peer-reviewed final drafts (postprints) for institutional record-keeping purposes. Access to that immediate postprint deposit in HypatiaSalud may be set immediately as ‘Open Access’ if copyright conditions allows; otherwise access can be set as ‘Closed Access’. International Standards application: HypatiaSalud will support OAI-PMH and DRIVER, to allow that central repositories could harvest its content or metadata. Development of human resources strategies in order to foster self-archiving through merit acknowledge and accreditation. Conclusions. It seems likely that setting up an Institutional Repository for the Public Health System in Andalucía would have substantial net benefits in the longer term for the Institution, despite the lag between the costs and realisation of benefits.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.