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1 to 50 of 217 results
  • A joint model for 2D and 3D pose estimation from a single image

     Simo Serra, Edgar; Quattoni, Ariadna Julieta; Torras, Carme; Moreno Noguer, Francesc d'Assis
    IEEE Conference on Computer Vision and Pattern Recognition
    Presentation's date: 2013-06-23
    Presentation of work at congresses

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    We introduce a novel approach to automatically recover 3D human pose from a single image. Most previous work follows a pipelined approach: initially, a set of 2D features such as edges, joints or silhouettes are detected in the image, and then these observations are used to infer the 3D pose. Solving these two problems separately may lead to erroneous 3D poses when the feature detector has performed poorly. In this paper, we address this issue by jointly solving both the 2D detection and the 3D inference problems. For this purpose, we propose a Bayesian framework that integrates a generative model based on latent variables and discriminative 2D part detectors based on HOGs, and perform inference using evolutionary algorithms. Real experimentation demonstrates competitive results, and the ability of our methodology to provide accurate 2D and 3D pose estimations even when the 2D detectors are inaccurate. © 2013 IEEE.

  • Turing's algorithmic lens: from computability to complexity theory

     Diaz Cort, Jose Maria; Torras, Carme
    Arbor (Madrid)
    Date of publication: 2013
    Journal article

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    The decidability question, i.e., whether any mathematical statement could be computationally proven true or false, was raised by Hilbert and remained open until Turing answered it in the negative. Then, most efforts in theoretical computer science turned to complexity theory and the need to classify decidable problems according to their difficulty. Among others, the classes P (problems solvable in polynomial time) and NP (problems solvable in non-deterministic polynomial time) were defined, and one of the most challenging scientific quests of our days arose: whether P = NP. This still open question has implications not only in computer science, mathematics and physics, but also in biology, sociology and economics, and it can be seen as a direct consequence of Turing¿s way of looking through the algorithmic lens at different disciplines to discover how pervasive computation is.

  • A robot learning from demonstration framework to perform force-based manipulation tasks

     Rozo Castañeda, Leonel; Jiménez Schlegl, Pablo; Torras, Carme
    Intelligent Service Robotics
    Date of publication: 2013
    Journal article

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    This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are manyfold. First, we deal with the problem of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human¿robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known what to imitate? problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher¿s demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher¿s one.

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    Dynamically consistent probabilistic model for robot motion learning  Open access

     Pardo Ayala, Diego Esteban; Rozo Castañeda, Leonel; Alenyà Ribas, Guillem; Torras, Carme
    Workshop on Learning and Interaction in Haptic Robots
    Presentation's date: 2012
    Presentation of work at congresses

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    This work presents a probabilistic model for learning robot tasks from human demonstrations using kinesthetic teaching. The difference with respect to previous works is that a complete state of the robot is used to obtain a consistent representation of the dynamics of the task. The learning framework is based on hidden Markov models and Gaussian mixture regression, used for coding and reproducing the skills. Benefits of the proposed approach are shown in the execution of a simple self-crossing trajectory by a 7-DoF manipulator.

  • External force estimation for textile grasp detection

     Colomé Figueras, Adrià; Pardo Ayala, Diego Esteban; Alenyà Ribas, Guillem; Torras, Carme
    IROS Workshop Beyond Robot Grasping: Modern Approaches for Learning Dynamic Manipulation
    Presentation's date: 2012
    Presentation of work at congresses

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  • Robotic leaf probing via segmentation of range data into surface patches

     Alenyà Ribas, Guillem; Dellen, Babette Karla Margarete; Foix Salmeron, Sergi; Torras, Carme
    IROS Workshop on Agricultural Robotics: Enabling Safe, Efficient, Affordable Robots for Food Production (IROS AGROBOTICS)
    Presentation's date: 2012
    Presentation of work at congresses

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    Object detection methods for robot grasping: Experimental assessment and tuning  Open access

     Rigual Aparici, Ferran; Ramisa Ayats, Arnau; Alenyà Ribas, Guillem; Torras, Carme
    International Conference of the Catalan Association for Artificial Intelligence
    Presentation's date: 2012
    Presentation of work at congresses

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    In this work we address the problem of object detection for the purpose of object manipulation in a service robotics scenario. Several implementations of state-of-the-art object detection methods were tested, and the one with the best performance was selected. During the evaluation, three main practical limitations of current methods were identified in relation with long-range object detection, grasping point detection and automatic learning of new objects; and practical solutions are proposed for the last two. Finally, the complete pipeline is evaluated in a real grasping experiment.

  • Redundant inverse kinematics: experimental comparative review and two enhancements

     Colomé Figueras, Adrià; Torras, Carme
    IEEE/RSJ International Conference on Intelligent Robots and Systems
    Presentation's date: 2012
    Presentation of work at congresses

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  • POMDP approach to robotized clothes separation

     Monsó Purtí, Pol; Alenyà Ribas, Guillem; Torras, Carme
    IEEE/RSJ International Conference on Intelligent Robots and Systems
    Presentation's date: 2012
    Presentation of work at congresses

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  • Characterization of textile grasping experiments

     Alenyà Ribas, Guillem; Ramisa Ayats, Arnau; Moreno Noguer, Francesc d'Assis; Torras, Carme
    IEEE International Conference on Robotics and Automation
    Presentation's date: 2012
    Presentation of work at congresses

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    Using depth and appearance features for informed robot grasping of highly wrinkled clothes  Open access

     Ramisa Ayats, Arnau; Alenyà Ribas, Guillem; Moreno Noguer, Francesc d'Assis; Torras, Carme
    IEEE International Conference on Robotics and Automation
    Presentation's date: 2012
    Presentation of work at congresses

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    Detecting grasping points is a key problem in cloth manipulation. Most current approaches follow a multiple regrasp strategy for this purpose, in which clothes are sequentially grasped from different points until one of them yields to a desired configuration. In this paper, by contrast, we circumvent the need for multiple re-graspings by building a robust detector that identifies the grasping points, generally in one single step, even when clothes are highly wrinkled. In order to handle the large variability a deformed cloth may have, we build a Bag of Features based detector that combines appearance and 3D geometry features. An image is scanned using a sliding window with a linear classifier, and the candidate windows are refined using a non-linear SVM and a “grasp goodness” criterion to select the best grasping point. We demonstrate our approach detecting collars in deformed polo shirts, using a Kinect camera. Experimental results show a good performance of the proposed method not only in identifying the same trained textile object part under severe deformations and occlusions, but also the corresponding part in other clothes, exhibiting a degree of generalization.

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    Information-gain view planning for free-form object reconstruction with a 3D ToF camera  Open access

     Foix Salmeron, Sergi; Kriegel, Simon; Fuchs, Stefan; Alenyà Ribas, Guillem; Torras, Carme
    International Conference on Advanced Concepts for Intelligent Vision Systems
    Presentation's date: 2012
    Presentation of work at congresses

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    Active view planning for gathering data from an unexplored 3D complex scenario is a hard and still open problem in the computer vision community. In this paper, we present a general task-oriented approach based on an information-gain maximization that easily deals with such a problem. Our approach consists of ranking a given set of possible actions, based on their task-related gains, and then executing the best-ranked action to move the required sensor. An example of how our approach behaves is demonstrated by applying it over 3D raw data for real-time volume modelling of complex-shaped objects. Our setting includes a calibrated 3D time-of-flight (ToF) camera mounted on a 7 degrees of freedom (DoF) robotic arm. Noise in the sensor data acquisition, which is too often ignored, is here explicitly taken into account by computing an uncertainty matrix for each point, and refining this matrix each time the point is seen again. Results show that, by always choosing the most informative view, a complete model of a 3D free-form object is acquired and also that our method achieves a good compromise between speed and precision.

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    Single image 3D human pose estimation from noisy observations  Open access

     Simo Serra, Edgar; Ramisa Ayats, Arnau; Torras, Carme; Alenyà Ribas, Guillem; Moreno Noguer, Francesc d'Assis
    IEEE Conference on Computer Vision and Pattern Recognition
    Presentation's date: 2012
    Presentation of work at congresses

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    Markerless 3D human pose detection from a single image is a severely underconstrained problem because different 3D poses can have similar image projections. In order to handle this ambiguity, current approaches rely on prior shape models that can only be correctly adjusted if 2D image features are accurately detected. Unfortunately, although current 2D part detector algorithms have shown promising results, they are not yet accurate enough to guarantee a complete disambiguation of the 3D inferred shape. In this paper, we introduce a novel approach for estimating 3D human pose even when observations are noisy. We propose a stochastic sampling strategy to propagate the noise from the image plane to the shape space. This provides a set of ambiguous 3D shapes, which are virtually undistinguishable from their image projections. Disambiguation is then achieved by imposing kinematic constraints that guarantee the resulting pose resembles a 3D human shape. We validate the method on a variety of situations in which state-of-the-art 2D detectors yield either inaccurate estimations or partly miss some of the body parts.

  • A personal account of Turing¿s imprint on the development of computer science

     Díaz, Josep; Torras, Carme
    Computer science review
    Date of publication: 2012
    Journal article

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    Kinematic Bézier maps  Open access

     Ulbrich, Stefan; Ruiz de Angulo Garcia, Vicente; Torras, Carme; Asfour, Tamim; Dillmann, Rudiger
    IEEE transactions on systems man and cybernetics Part B-Cybernetics
    Date of publication: 2012
    Journal article

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    The kinematics of a robot with many degrees of freedom is a very complex function. Learning this function for a large workspace with a good precision requires a huge number of training samples, i.e., robot movements. In this paper, we introduce the Kinematic Bézier Map (KB-Map), a parameterizable model without the generality of other systems but whose structure readily incorporates some of the geometric constraints of a kinematic function. In this way, the number of training samples required is drastically reduced. Moreover, the simplicity of the model reduces learning to solving a linear least squares problem. Systematic experiments have been carried out showing the excellent interpolation and extrapolation capabilities of KB-Maps and their relatively low sensitivity to noise.

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    General robot kinematics decomposition without intermediate markers  Open access

     Ulbrich, Stefan; Ruiz de Angulo Garcia, Vicente; Asfour, Tamim; Torras, Carme; Dillmann, Rüdiger
    IEEE transactions on neural networks
    Date of publication: 2012
    Journal article

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    The calibration of serial manipulators with high numbers of degrees of freedom by means of machine learning is a complex and time-consuming task. With the help of a simple strategy, this complexity can be drastically reduced and the speed of the learning procedure can be increased: When the robot is virtually divided into shorter kinematic chains, these subchains can be learned separately and, hence, much more efficiently than the complete kinematics. Such decompositions, however, require either the possibility to capture the poses of all endeffectors of all subchains at the same time, or they are limited to robots that fulfill special constraints. In this work, an alternative decomposition is presented that does not suffer from these limitations. An offline training algorithm is provided in which the composite subchains are learned sequentially with dedicated movements. A second training scheme is provided to train composite chains simultaneously and online. Both schemes can be used together with many machine learning algorithms. In the simulations, an algorithm using Parameterized Self-Organizing Maps (PSOM) modified for online learning and Gaussian Mixture Models (GMM) were chosen to show the correctness of the approach. The experimental results show that, using a two-fold decomposition, the number of samples required to reach a given precision

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  • Efficient approaches for object class detection  Open access

     Villamizar Vergel, Michael Alejandro
    Defense's date: 2012-09-18
    Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya
    Theses

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    La visión por computador y más específicamente el reconocimiento de objetos han demostrado en los últimos años un impresionante progreso que ha llevado a la aparición de nuevas y útiles tecnologías que facilitan nuestras actividades diarias y mejoran ciertos procesos industriales. Actualmente, nosotros podemos encontrar algoritmos para el reconocimiento de objetos en computadores, videocámaras, teléfonos móviles, tablets o sitios web para la realización de ciertas tareas específicas tales como la detección de caras, el reconocimiento de gestos y escenas, la detección de peatones, la realidad aumentada, etc. No obstante, estas aplicaciones siguen siendo problemas abiertos que cada año reciben más atención por parte de la comunidad de visión por computador. Esto se demuestra por el hecho de que cientos de artículos abordando estos problemas son publicados en congresos internacionales y revistas anualmente. Desde una perspectiva general, los trabajos más recientes intentan mejorar el desempeño de clasificadores, hacer frente a nuevos y más desafiantes problemas de detección, y a aumentar la eficiencia computacional de los algoritmos resultantes con el objetivo de ser implementados comercialmente en diversos dispositivos electrónicos. Aunque actualmente, existen enfoques robustos y confiables para la detección de objetos, la mayoría de estos métodos tienen un alto coste computacional que hacen imposible su aplicación en tareas en tiempo real. En particular, el coste computacional y el desempeño de cualquier sistema de reconocimiento está determinado por el tipo de características, método de reconocimiento y la metodología utilizada para localizar los objetos dentro de las imágenes. El principal objetivo de estos métodos es obtener sistemas de detección eficaces pero también eficientes. A través de esta tesis diferentes enfoques son presentados para abordar de manera eficiente y discriminante la detección de objetos en condiciones de imagen diversas y difíciles. Cada uno de los enfoques propuestos ha sido especialmente diseñado y enfocado para la detección de objetos en circunstancias distintas, tales como la categorización de objetos, la detección bajo rotaciones en el plano o la detección de objetos a partir de múltiples vistas. Los métodos propuestos combinan varias ideas y técnicas para la obtención de detectores de objetos que son tanto altamente discriminantes como eficientes. Esto se demuestra experimentalmente en varias bases de datos del estado del arte donde los resultados alcanzados son competitivos al ser contrastados con otros métodos recientes. En concreto, esta tesis estudia y desarrolla características rápidas, algoritmos de aprendizaje, métodos para reducir el coste computacional de los clasificadores y representaciones de imagen integral que permiten un mejor cálculo de las características.

    Computer vision and more specifically object recognition have demonstrated in recent years an impressive progress that has led to the emergence of new and useful technologies that facilitate daily activities and improve some industrial processes. Currently, we can find algorithms for object recognition in computers, video cameras, mobile phones, tablets or websites, for the accomplishment of specific tasks such as face detection, gesture and scene recognition, detection of pedestrians, augmented reality, etc. However, these applications are still open problems that each year receive more attention in the computer vision community. This is demonstrated by the fact that hundreds of articles addressing these problems are published in international conferences and journals annually. In a broader view, recent work attempts to improve the performance of classifiers, to face new and more challenging problems of detection and to increase the computational efficiency of the resulting algorithms in order to be implemented commercially in diverse electronic devices. Although nowadays there are robust and reliable approaches for detecting objects, most of these methods have a high computational cost that make impossible their application for real-time tasks. In particular, the computational cost and performance of any recognition system is determined by the type of features, the method of recognition and the methodology used for localizing objects within images. The main objective of these methods is to produce not only effective but also efficient detection systems. Through this dissertation different approaches are presented for addressing efficiently and discriminatively the detection of objects in diverse and difficult imaging conditions. Each one of the proposed approaches are especially designed and focus on different detection problems, such as object categorization, detection under rotations in the plane or the detection of objects from multiple views. The proposed methods combine several ideas and techniques for obtaining object detectors that are both highly discriminative and efficient. This is demonstrated experimentally in several state-of-the-art databases where our results are competitive with other recent and successful methods. In particular, this dissertation studies and develops fast features, learning algorithms, methods for reducing the computational cost of the classifiers and integral image representations for speeding up feature computation.

  • Robot learning from demonstration of force-based tasks with multiple solution trajectories

     Rozo Castañeda, Leonel; Jimenez Schlegl, Pablo; Torras, Carme
    International Conference on Advanced Robotics
    Presentation's date: 2011
    Presentation of work at congresses

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    Segmenting color images into surface patches by exploiting sparse depth data  Open access

     Dellen, Babette Karla Margarete; Alenyà Ribas, Guillem; Foix Salmeron, Sergi; Torras, Carme
    Winter Vision Meeting: Workshop on Applications of Computer Vision
    Presentation's date: 2011
    Presentation of work at congresses

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    We present a new method for segmenting color images into their composite surfaces by combining color segmentation with model-based fitting utilizing sparse depth data, acquired using time-of-flight (Swissranger, PMD CamCube) and stereo techniques. The main target of our work is the segmentation of plant structures, i.e., leaves, from color-depth images, and the extraction of color and 3D shape information for automating manipulation tasks. Since segmentation is performed in the dense color space, even sparse, incomplete, or noisy depth information can be used. This kind of data often represents a major challenge for methods operating in the 3D data space directly. To achieve our goal, we construct a three-stage segmentation hierarchy by segmenting the color image with different resolutions-assuming that “true” surface boundaries must appear at some point along the segmentation hierarchy. 3D surfaces are then fitted to the color-segment areas using depth data. Those segments which minimize the fitting error are selected and used to construct a new segmentation. Then, an additional region merging and a growing stage are applied to avoid over-segmentation and label previously unclustered points. Experimental results demonstrate that the method is successful in segmenting a variety of domestic objects and plants into quadratic surfaces. At the end of the procedure, the sparse depth data is completed using the extracted surface models, resulting in dense depth maps. For stereo, the resulting disparity maps are compared with ground truth and the average error is computed.

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    Determining where to grasp cloth using depth information  Open access

     Ramisa Ayats, Arnau; Alenyà Ribas, Guillem; Moreno Noguer, Francesc d'Assis; Torras, Carme
    International Conference of the Catalan Association for Artificial Intelligence
    Presentation's date: 2011
    Presentation of work at congresses

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    In this paper we address the problem of finding an initial good grasping point for the robotic manipulation of textile objects lying on a flat surface. Given as input a point cloud of the cloth acquired with a 3D camera, we propose choosing as grasping points those that maximize a new measure of wrinkledness, computed from the distribution of normal directions over local neighborhoods. Real grasping experiments using a robotic arm are performed, showing that the proposed measure leads to promising results.

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  • 3D modelling of leaves from color and ToF data for robotized plant measuring

     Alenyà Ribas, Guillem; Dellen, Babette Karla Margarete; Torras, Carme
    IEEE International Conference on Robotics and Automation
    Presentation's date: 2011
    Presentation of work at congresses

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    Supervision of long-lasting extensive botanic experiments is a promising robotic application that some recent technological advances have made feasible. Plant modelling for this application has strong demands, particularly in what concerns 3D information gathering and speed. This paper shows that Time-of-Flight (ToF) cameras achieve a good compromise between both demands, providing a suitable complement to color vision. A new method is proposed to segment plant images into their composite surface patches by combining hierarchical color segmentation with quadratic surface fitting using ToF depth data. Experimentation shows that the interpolated depth maps derived from the obtained surfaces fit well the original scenes. Moreover, candidate leaves to be approached by a measuring instrument are ranked, and then robot-mounted cameras move closer to them to validate their suitability to being sampled. Some ambiguities arising from leaves overlap or occlusions are cleared up in this way. The work is a proof-of-concept that dense color data combined with sparse depth as provided by a ToF camera yields a good enough 3D approximation for automated plant measuring at the high throughput imposed by the application.

  • New geometric approaches to the singularity analysis of parallel platforms

     Borras Sol, Julia; Thomas Arroyo, Federico; Torras, Carme
    Workshop Español de Robótica
    Presentation's date: 2011
    Presentation of work at congresses

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  • Symmetry breaking in numeric constraint problems

     Goldsztejn, Alexandre; Jermann, Christophe; Ruiz de Angulo Garcia, Vicente; Torras, Carme
    International Conference on Principles and Practice of Constraint Programming
    Presentation's date: 2011
    Presentation of work at congresses

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    Towards plant monitoring through next best view  Open access

     Foix Salmeron, Sergi; Alenyà Ribas, Guillem; Torras, Carme
    International Conference of the Catalan Association for Artificial Intelligence
    Presentation's date: 2011
    Presentation of work at congresses

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    Monitoring plants using leaf feature detection is a challenging perception task because different leaves, even from the same plant, may have very different shapes, sizes and deformations. In addition, leaves may be occluded by other leaves making it hard to determine some of their characteristics. In this paper we use a Time-of-Flight (ToF) camera mounted on a robot arm to acquire the depth information needed for plant leaf detection. Under a Next Best View (NBV) paradigm, we propose a criterion to compute a new camera position that offers a better view of a target leaf. The proposed criterion exploits some typical errors of the ToF camera, which are common to other 3D sensing devices as well. This approach is also useful when more than one leaf is segmented as the same region, since moving the camera following the same NBV criterion helps to disambiguate this situation.

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    Active perception of deformable objects using 3D cameras  Open access

     Alenyà Ribas, Guillem; Moreno Noguer, Francesc d'Assis; Ramisa Ayats, Arnau; Torras, Carme
    Workshop Español de Robótica
    Presentation's date: 2011
    Presentation of work at congresses

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    Perception and manipulation of rigid objects has received a lot of attention, and several solutions have been proposed. In contrast, dealing with deformable objects is a relatively new and challenging task because they are more complex to model, their state is difficult to determine, and self-occlusions are common and hard to estimate. In this paper we present our progress/results in the perception of deformable objects both using conventional RGB cameras and active sensing strategies by means of depth cameras.We provide insights in two different areas of application: grasping of textiles and plant leaf modelling.

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    Integrating task planning and interactive learning for robots to work in human environments  Open access

     Agostini, Alejandro Gabriel; Torras, Carme; Wörgötter, Florentin
    International Joint Conference on Artificial Intelligence
    Presentation's date: 2011
    Presentation of work at congresses

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    Human environments are challenging for robots, which need to be trainable by lay people and learn new behaviours rapidly without disrupting much the ongoing activity. A system that integrates AI techniques for planning and learning is here proposed to satisfy these strong demands. The approach rapidly learns planning operators from few action experiences using a competitive strategy where many alternatives of cause-effect explanations are evaluated in parallel, and the most successful ones are used to generate the operators. The success of a cause-effect explanation is evaluated by a probabilistic estimate that compensates the lack of experience, producing more confident estimations and speeding up the learning in relation to other known estimates. The system operates without task interruption by integrating in the planning-learning loop a human teacher that supports the planner in making decisions. All the mechanisms are integrated and synchronized in the robot using a general decision-making framework. The feasibility and scalability of the architecture are evaluated in two different robot platforms: a Stäubli arm, and the humanoid ARMAR III.

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  • Architectural singularities of a class of pentapods

     Borras Sol, Julia; Thomas Arroyo, Federico; Torras, Carme
    Mechanism and machine theory
    Date of publication: 2011
    Journal article

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  • Desplegament de l'exposició RSME imaginari a Catalunya. Fase 2011

     Xambó Descamps, Sebastian; Plans Berenguer, Bernat; Borras Sol, Julia; Barja Yañez, Miguel Angel; Thomas Arroyo, Federico; Quer Bosor, Jordi; Torras, Carme; Alberich Carramiñana, Maria
    Participation in a competitive project

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  • Taller d'Humanoides

     Hernandez Juan, Sergi; Alenyà Ribas, Guillem; Torras, Carme
    Participation in a competitive project

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  • Robot learning from demonstration in the force domain

     Rozo Castañeda, Leonel; Jimenez Schlegl, Pablo; Torras, Carme
    IJCAI Workshop on Agents Learning Interactively from Human Teachers
    Presentation's date: 2011
    Presentation of work at congresses

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    Researchers are becoming aware of the importance of other information sources besides visual data in robot learning by demonstration (LbD). Forcebased perceptions are shown to convey very relevant information – missed by visual and position sensors – for learning specific tasks. In this paper, we review some recent works using forces as input data in LbD and Human-Robot interaction (HRI) scenarios, and propose a complete learning framework for teaching force-based manipulation skills to a robot through a haptic device. We suggest to use haptic interfaces not only as a demonstration tool but also as a communication channel between the human and the robot, getting the teacher more involved in the teaching process by experiencing the force signals sensed by the robot. Within the proposed framework, we provide solutions for treating force signals, extracting relevant information about the task, encoding the training data and generalizing to perform successfully under unknown conditions.

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    A general strategy for interactive decision-making in robotic platforms  Open access

     Agostini, Alejandro Gabriel; Torras, Carme; Wörgötter, Florentin
    Date: 2011
    Report

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    This work presents an intergated strategy for planning and learning suitable to execute tasks with robotic platforms without any previous task specification. The approach rapidly learns planning operators from few action experiences using a competitive strategy where many alternatives of cause-effect explanations are evaluated in parallel, and the most successful ones are used to generate the operators. The system operates without task interruption by integrating in the planning-learning loop a human teacher that supports the planner in making decisions. All the mechanisms are integrated and synchronized in the robot using a general decision-making framework.

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    Lock-in time-of-flight (ToF) cameras: a survey  Open access

     Foix Salmeron, Sergi; Alenyà Ribas, Guillem; Torras, Carme
    IEEE sensors journal
    Date of publication: 2011
    Journal article

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    This paper reviews the state-of-the art in the field of lock-in ToF cameras, their advantages, their limitations, the existing calibration methods, and the way they are being used, sometimes in combination with other sensors. Even though lockin ToF cameras provide neither higher resolution nor larger ambiguity-free range compared to other range map estimation systems, advantages such as registered depth and intensity data at a high frame rate, compact design, low weight and reduced power consumption have motivated their increasing usage in several research areas, such as computer graphics, machine vision and robotics.

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  • Singularity-invariant families of line-plane 5-SPU platforms

     Borras Sol, Julia; Thomas Arroyo, Federico; Torras, Carme
    IEEE transactions on robotics
    Date of publication: 2011-10
    Journal article

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    Singularity-invariant leg rearrangements in Stewart-Gough platforms  Open access

     Borras Sol, Julia; Thomas Arroyo, Federico; Torras, Carme
    International Symposium on Advances in Robot Kinematics
    Presentation's date: 2010
    Presentation of work at congresses

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    Singularity-invariant leg rearrangements in doubly-planar Stewart-Gough platforms  Open access

     Borras Sol, Julia; Thomas Arroyo, Federico; Torras, Carme
    Robotics: Science and Systems
    Presentation's date: 2010
    Presentation of work at congresses

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    In general, rearranging the legs of a Stewart-Gough platform, i.e., changing the locations of its leg attachments, modifies the platform singularity locus in a rather unexpected way. Nevertheless, some leg rearrangements have been recently found to leave singularities invariant but, unfortunately, these rearrangements are only valid for Stewart-Gough platforms containing rigid components. In this work, the authors go a step further presenting singularity-invariant leg rearrangements that can be applied to any Stewart-Gough platform whose base and platform attachments are coplanar. The practical consequences of the presented theoretical results are illustrated with several examples including well-known architectures.

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  • Learning force-based robot skills from haptic demonstration

     Rozo Castañeda, Leonel; Jimenez Schlegl, Pablo; Torras, Carme
    International Conference of the Catalan Association for Artificial Intelligence
    Presentation's date: 2010
    Presentation of work at congresses

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    A family of quadratically-solvable 5-SPU parallel robots  Open access

     Borras Sol, Julia; Thomas Arroyo, Federico; Torras, Carme
    IEEE International Conference on Robotics and Automation
    Presentation's date: 2010
    Presentation of work at congresses

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    A 5-SPU robot with collinear universal joints is well suited to handling an axisymmetric tool, since it has 5 controllable DoFs and the remaining one is a free rotation around the tool. The kinematics of such a robot having also coplanar spherical joints has previously been studied as a rigid subassembly of a Stewart-Gough platform, it being denoted a line-plane component. It was shown that this component has 8 assembly modes corresponding to the roots of a bi-quartic polynomial. Here we identify a whole family of these 5-SPU robots having only 4 assembly modes, which are obtained by solving two quadratic equations. This family is defined by a simple proportionality constraint relating the coordinates of the base and platform attachments. A geometric interpretation of the architectural singularities of this type of robots in terms of conics is provided, which facilitates their avoidance at the design stage. Parallel singularities obey also a neat geometric structure, which permits deriving a cell decomposition of configuration space. Two practical features of these quadratically-solvable robots are the large maneuverability within each connected component and the fact that, for a fixed orientation of the tool, the singularity locus reduces to a plane. Index Terms—Parallel manipulators, Stewart-Gough platforms, robot kinematics, kinematics singularities.

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    Sharpening haptic inputs for teaching a manipulation skill to a robot  Open access

     Rozo Castañeda, Leonel; Jimenez Schlegl, Pablo; Torras, Carme
    IEEE International Conference on Applied Bionics and Biomechanics
    Presentation's date: 2010
    Presentation of work at congresses

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    Gaussian mixtures-based learning algorithms are suitable strategies for trajectory learning and skill acquisition, in the context of programming by demonstration (PbD). Input streams other than visual information, as used in most applications up to date, reveal themselves as quite useful in trajectory learning experiments where visual sources are not available. In this work we have used force/torque feedback through a haptic device for teaching a teleoperated robot to empty a rigid container. Structure vibrations and container inertia appeared to considerably disrupt the sensing process, so a filtering algorithm had to be devised. Moreover, some input variables seemed much more relevant to the particular task to be learned than others, which lead us to analyze the training data in order to select those relevant features through principal component analysis and a mutual information criterion. Then, a batch version of GMM/GMR [1], [2] was implemented using different training datasets (original, pre-processed data through PCA and MI). Tests where the teacher was instructed to follow a strategy compared to others where he was not lead to useful conclusions that permit devising the new research stages.

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    Object modeling using a ToF camera under an uncertainty reduction approach  Open access

     Foix Salmeron, Sergi; Alenyà Ribas, Guillem; Andrade Cetto, Juan; Torras, Carme
    IEEE International Conference on Robotics and Automation
    Presentation's date: 2010
    Presentation of work at congresses

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    Time-of-Flight (ToF) cameras deliver 3D images at 25 fps, offering great potential for developing fast object modeling algorithms. Surprisingly, this potential has not been extensively exploited up to now. A reason for this is that, since the acquired depth images are noisy, most of the available registration algorithms are hardly applicable. A further difficulty is that the transformations between views are in general not accurately known, a circumstance that multi-view object modeling algorithms do not handle properly under noisy conditions. In this work, we take into account both uncertainty sources (in images and camera poses) to generate spatially consistent 3D object models fusing multiple views with a probabilistic approach. We propose a method to compute the covariance of the registration process, and apply an iterative state estimation method to build object models under noisy conditions.

  • Robbie, the pioneer robot nanny: science fiction helps develop ethical social opinion

     Torras, Carme
    Interaction studies
    Date of publication: 2010
    Journal article

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    Camera motion estimation by tracking contour deformation: precision analysis  Open access

     Alenyà Ribas, Guillem; Torras, Carme
    Image and vision computing
    Date of publication: 2010-03
    Journal article

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    An algorithm to estimate camera motion from the progressive deformation of a tracked contour in the acquired video stream has been previously proposed. It relies on the fact that two views of a plane are related by an affinity, whose six parameters can be used to derive the six degrees-of-freedom of camera motion between the two views. In this paper we evaluate the accuracy of the algorithm. Monte Carlo simulations show that translations parallel to the image plane and rotations about the optical axis are better recovered than translations along this axis, which in turn are more accurate than rotations out of the plane. Concerning covariances, only the three less precise degrees-of-freedom appear to be correlated. In order to obtain means and covariances of 3D motions quickly on a working robot system, we resort to the Unscented Transformation (UT) requiring only 13 samples per view, after validating its usage through the previous Monte Carlo simulations. Two sets of experiments have been performed: short-range motion recovery has been tested using a Staübli robot arm in a controlled lab setting, while the precision of the algorithm when facing long translations has been assessed by means of a vehicle-mounted camera in a factory floor. In the latter more unfavourable case, the obtained errors are around 3%, which seems accurate enough for transferring operations

  • DESPLEGAMENT DE L'EXPOSICIÓ RSME-IMAGINARY A CATALUNYA - FASE 2010

     Xambó Descamps, Sebastian; Torras, Carme; Thomas Arroyo, Federico; Barja Yañez, Miguel Angel; Plans Berenguer, Bernat; Quer Bosor, Jordi; Borras Sol, Julia; Alberich Carramiñana, Maria
    Participation in a competitive project

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  • Quick learning of cause-effects relevant for robot action

     Agostini, Alejandro Gabriel; Wörgötter, Florentin; Torras, Carme
    Date: 2010
    Report

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    Exploitation of time-of-flight (ToF) cameras  Open access

     Foix Salmeron, Sergi; Alenyà Ribas, Guillem; Torras, Carme
    Date: 2010
    Report

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    This technical report reviews the state-of-the art in the field of ToF cameras, their advantages, their limitations, and their present-day applications sometimes in combination with other sensors. Even though ToF cameras provide neither higher resolution nor larger ambiguity-free range compared to other range map estimation systems, advantages such as registered depth and intensity data at a high frame rate, compact design, low weight and reduced power consumption have motivated their use in numerous areas of research. In robotics, these areas range from mobile robot navigation and map building to vision-based human motion capture and gesture recognition, showing particularly a great potential in object modeling and recognition.

  • Stratifications of the Euclidean motion group with applications to robotics

     Alberich Carramiñana, Maria; Gonzalez, V.; Thomas Arroyo, Federico; Torras, Carme
    Geometricae dedicata
    Date of publication: 2009-08
    Journal article

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    Partially flagged parallel manipulators: singularity charting and avoidance  Open access

     Alberich Carramiñana, Maria; Garolera, M; Thomas Arroyo, Federico; Torras, Carme
    IEEE transactions on robotics
    Date of publication: 2009-08
    Journal article

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    There are only three 6-SPS parallelmanipulatorswith triangular base and platform, i.e., the octahedral, the flagged, and the partially flagged, which are studied in this paper. The forward kinematics of the octahedralmanipulator is algebraically intricate, while those of the other two can be solved by three trilaterations. As an additional nice feature, the flagged manipulator is the only parallel platform for which a cell decomposition of its singularity locus has been derived. Here, we prove that the partially flagged manipulator also admits a well-behaved decomposition, technically called a stratification, some of whose strata are not topological cells, however. Remarkably, the adjacency diagram of the 5-D and 6-D strata (which shows what 5-D strata are contained in the closure of a 6-D one) is the same as for the flaggedmanipulator. The availability of such a decomposition permits devising a redundant 7-SPS manipulator, combining two partially flagged ones, which admits a control strategy that completely avoids singularities. Simulation results support these claims.

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    Straightening-free algorithm for the singularity analysis of Stewart-Gough platforms with collinear/coplanar attachments  Open access

     Borras Sol, Julia; Thomas Arroyo, Federico; Torras, Carme
    Computational Kinematics
    Presentation's date: 2009-05-08
    Presentation of work at congresses

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    An algorithm to derive the pure condition of any double-planar Stewart-Gough platform in a standard form suitable for comparison is presented. By applying the multilinear properties of brackets directly to the superbracket encoding of the pure condition, no straightening is required. It is then shown that any 3-3 platform has a corresponding 6-6 platform having its same superbracket, meaning that they have identical singularity loci. In general, the superbracket of any doubleplanar platform can be written as a linear combination of the superbrackets of 3-3 platforms, leading to a direct singularity assessment by inspecting the resulting decomposition.

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    On ¿-transforms  Open access

     Thomas Arroyo, Federico; Torras, Carme; Borras Sol, Julia
    IEEE transactions on robotics
    Date of publication: 2009
    Journal article

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    Anyset of two legs in a Gough–Stewart platform sharing an attachment is defined as a Δcomponent. This component links a point in the platform (base) to a line in the base (platform). Thus, if the two legs, which are involved in a Δ component, are rearranged without altering the location of the line and the point in their base and platform local reference frames, the singularity locus of the Gough–Stewart platform remains the same, provided that no architectural singularities are introduced. Such leg rearrangements are defined as Δ-transforms, and they can be applied sequentially and simultaneously. Although it may seem counterintuitive at first glance, the rearrangement of legs using simultaneous Δ-transforms does not necessarily lead to leg configurations containing a Δcomponent. As a consequence, the application of Δ-transforms reveals itself as a simple, yet powerful, technique for the kinematic analysis of large families of Gough–Stewart platforms. It is also shown that these transforms shed new light on the characterization of architectural singularities and their associated self-motions.

  • On Delta-Transforms

     Borras Sol, Julia; Thomas Arroyo, Federico; Torras, Carme
    IEEE transactions on robotics
    Date of publication: 2009-12
    Journal article

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