906 research outputs found

    Exploratory Action Selection to Learn Object Properties Through Robot Manipulation

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    Výber prieskumných akcií je pojem popisujúci proces autonómnej selekcie krokov, ktoré vedú agenta k predurčenému cieľu. V tejto práci, je cieľom skúmanie vlastností a celkovo kategórie daného objektu (napr. materiál, krabica, šálka a pod.) robotickým manipulátorom. Extrahovať vlastnosti objektu len vizuálne je limitujúce, najmä v spojitosti s fyzikálnymi/materiálnymi vlastnosťami ako povrchové trenie, tuhosť, či hmotnosť. V rámci tejto práce je hlavným interaktívnym prvkom dotyk, teda najviac informácii je získavaných z haptickej manipulácie predmetom. Narozdiel od vizuálnych vnemov, ktoré sú pasívne---fotografie zaobstarané statickou kamerou---haptické skúmanie je v samotnej podstate aktívne: spôsob manipulácie priamo ovplyvňuje množstvo informácií, ktoré je možné získať. V tejto práci je táto idea sformalizovaná, kde sú volené ďalšie robotické akcie (stláčanie, či dvíhanie objektov) na základe toho, ako je pravdepodobné, že na základe danej akcie príde k zníženiu neistoty v rámci vlastností--teda na základe ich očakávaného informačného zisku. Akcia, ktorá prináša informácií najviac, je zvolená. Očakávaný informačný zisk je počítaný v troch rôznych módoch založených na informačnej entropii. Informačná entropia je odhadovaná ako pre diskrétne pravdepodobnostné rozdelenie materiálovej kategórie, tak i pre spojité pravdepodobnostné rozdelenie vlastností, ako pružnosť, či hustota. Používame klasifikáciu ako proxy metriku toho, ako veľmi sú rozhodnutia algoritmu ohľadne selekcie akcií optimálne. Mód optimalizujúci pre informačný zisk spojitej premennej vykazuje najlepšie výsledky. Učenie sa vlastností objektov je zabezpečené pomocou Bayesovskej aktualizácie z meraní priamo manipulátorom. Takýto výber akcií vedie k viac efektívnemu učenie o okolí a ako výsledok pomáha agentovi v navigácií reálnym svetom, kde je potrebné očakávať aj neočakávané.Action selection is a term used to describe a process of autonomous selection of steps that lead an agent to a predetermined goal. In this work, discovering object properties and the overall object category (e.g., material, or box, mug, etc.) by a robot manipulator is the desired goal. Extracting properties of objects from visual input only is limited, especially regarding physical/material properties like surface roughness, stiffness, or mass. Here, haptic exploration, i.e., mainly proprioceptive and tactile input during manipulation of the object, is indispensable. Furthermore, unlike visual sensing, which is often passive---images taken by a static camera---haptic exploration is intrinsically active: the particular way of manipulating the object determines the quality of information that can be acquired. Here, this idea is formalized, and robot actions (compressing or lifting objects) are assessed by how much they are likely to reduce uncertainty about specific object properties---their expected information gain. The most informative action is then chosen. The expected information gain is calculated in three different modes based on information entropy, which is estimated for both discrete probability distribution of material composition of the object (e.g., plastic, ceramics, metal) and continuous distribution of each property like elasticity or density. We use classification as a proxy metric of how optimal are the choices of the action selection algorithm. Overall the mode optimizing for the information gain of the continuous properties results in the best classification. Learning of object properties is accomplished in the form of a Bayesian update from real measurement actions. Such selection of actions leads to more efficient learning about the environment and, as a result, helps the agent in navigating the real world, where the unexpected shall be expected

    A Biologically Inspired Controllable Stiffness Multimodal Whisker Follicle

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    This thesis takes a soft robotics approach to understand the computational role of a soft whisker follicle with mechanisms to control the stiffness of the whisker. In particular, the thesis explores the role of the controllable stiffness whisker follicle to selectively favour low frequency geometric features of an object or the high frequency texture features of the object.Tactile sensing is one of the most essential and complex sensory systems for most living beings. To acquire tactile information and explore the environment, animals use various biological mechanisms and transducing techniques. Whiskers, or vibrissae are a form of mammalian hair, found on almost all mammals other than homo sapiens. For many mammals, and especially rodents, these whiskers are essential as a means of tactile sensing.The mammalian whisker follicle contains multiple sensory receptors strategically organised to capture tactile sensory stimuli of different frequencies via the vibrissal system. Nocturnal mammals such as rats heavily depend on whisker based tactile perception to find their way through burrows and identify objects. There is diversity in the whiskers in terms of the physical structure and nervous innervation. The robotics community has developed many different whisker sensors inspired by this biological basis. They take diverse mechanical, electronic, and computational approaches to use whiskers to identify the geometry, mechanical properties, and objects' texture. Some work addresses specific object identification features and others address multiple features such as texture and shape etc. Therefore, it is vital to have a comprehensive discussion of the literature and to understand the merits of bio-inspired and pure-engineered approaches to whisker-based tactile perception.The most important contribution is the design and use of a novel soft whisker follicle comprising two different frequency-dependent data capturing modules to derive more profound insights into the biological basis of tactile perception in the mammalian whisker follicle. The new insights into the biological basis of tactile perception using whiskers provide new design guidelines to develop efficient robotic whiskers

    Joint Entropy-Based Morphology Optimization of Soft Strain Sensor Networks for Functional Robustness

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    Dense and distributed tactile sensors are critical for robots to achieve human-like manipulation skills. Soft robotic sensors are a potential technological solution to obtain the required high dimensional sensory information unobtrusively. However, the design of this new class of sensors is still based on human intuition or derived from traditional flex sensors. This work is a first step towards automated design of soft sensor morphologies based on optimization of information theory metrics and machine learning. Elementary simulation models are used to develop the optimized sensor morphologies that are more accurate and robust with the same number of sensors. Same configurations are replicated experimentally to validate the feasibility of such an approach for practical applications. Furthermore, we present a novel technique for drift compensation in soft strain sensors that allows us to obtain accurate contact localization. This work is an effort towards transferring the paradigm of \textit {morphological computation} from soft actuator designing to soft sensor designing for high performance, resilient tactile sensory networks.uture and Emerging Technologies (FET) programme of the European Commission (grant agreement ID 828818)

    Gaussian Processes for Machine Learning in Robotics

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    Mención Internacional en el título de doctorNowadays, machine learning is widely used in robotics for a variety of tasks such as perception, control, planning, and decision making. Machine learning involves learning, reasoning, and acting based on the data. This is achieved by constructing computer programs that process the data, extract useful information or features, make predictions to infer unknown properties, and suggest actions to take or decisions to make. This computer program corresponds to a mathematical model of the data that describes the relationship between the variables that represent the observed data and properties of interest. The aforementioned model is learned based on the available training data, which is accomplished using a learning algorithm capable of automatically adjusting the parameters of the model to agree with the data. Therefore, the architecture of the model needs to be selected accordingly, which is not a trivial task and usually depends on the machine-learning engineer’s insights and past experience. The number of parameters to be tuned varies significantly with the selected machine learning model, ranging from two or three parameters for Gaussian processes (GP) to hundreds of thousands for artificial neural networks. However, as more complex and novel robotic applications emerge, data complexity increases and prior experience may be insufficient to define adequate mathematical models. In addition, traditional machine learning methods are prone to problems such as overfitting, which can lead to inaccurate predictions and catastrophic failures in critical applications. These methods provide probabilistic distributions as model outputs, allowing for estimating the uncertainty associated with predictions and making more informed decisions. That is, they provide a mean and variance for the model responses. This thesis focuses on the application of machine learning solutions based on Gaussian processes to various problems in robotics, with the aim of improving current methods and providing a new perspective. Key areas such as trajectory planning for unmanned aerial vehicles (UAVs), motion planning for robotic manipulators and model identification of nonlinear systems are addressed. In the field of path planning for UAVs, algorithms based on Gaussian processes that allow for more efficient planning and energy savings in exploration missions have been developed. These algorithms are compared with traditional analytical approaches, demonstrating their superiority in terms of efficiency when using machine learning. Area coverage and linear coverage algorithms with UAV formations are presented, as well as a sea surface search algorithm. Finally, these algorithms are compared with a new method that uses Gaussian processes to perform probabilistic predictions and optimise trajectory planning, resulting in improved performance and reduced energy consumption. Regarding motion planning for robotic manipulators, an approach based on Gaussian process models that provides a significant reduction in computational times is proposed. A Gaussian process model is used to approximate the configuration space of a robot, which provides valuable information to avoid collisions and improve safety in dynamic environments. This approach is compared to conventional collision checking methods and its effectiveness in terms of computational time and accuracy is demonstrated. In this application, the variance provides information about dangerous zones for the manipulator. In terms of creating models of non-linear systems, Gaussian processes also offer significant advantages. This approach is applied to a soft robotic arm system and UAV energy consumption models, where experimental data is used to train Gaussian process models that capture the relationships between system inputs and outputs. The results show accurate identification of system parameters and the ability to make reliable future predictions. In summary, this thesis presents a variety of applications of Gaussian processes in robotics, from trajectory and motion planning to model identification. These machine learning-based solutions provide probabilistic predictions and improve the ability of robots to perform tasks safely and efficiently. Gaussian processes are positioned as a powerful tool to address current challenges in robotics and open up new possibilities in the field.El aprendizaje automático ha revolucionado el campo de la robótica al ofrecer una amplia gama de aplicaciones en áreas como la percepción, el control, la planificación y la toma de decisiones. Este enfoque implica desarrollar programas informáticos que pueden procesar datos, extraer información valiosa, realizar predicciones y ofrecer recomendaciones o sugerencias de acciones. Estos programas se basan en modelos matemáticos que capturan las relaciones entre las variables que representan los datos observados y las propiedades que se desean analizar. Los modelos se entrenan utilizando algoritmos de optimización que ajustan automáticamente los parámetros para lograr un rendimiento óptimo. Sin embargo, a medida que surgen aplicaciones robóticas más complejas y novedosas, la complejidad de los datos aumenta y la experiencia previa puede resultar insuficiente para definir modelos matemáticos adecuados. Además, los métodos de aprendizaje automático tradicionales son propensos a problemas como el sobreajuste, lo que puede llevar a predicciones inexactas y fallos catastróficos en aplicaciones críticas. Para superar estos desafíos, los métodos probabilísticos de aprendizaje automático, como los procesos gaussianos, han ganado popularidad. Estos métodos ofrecen distribuciones probabilísticas como salidas del modelo, lo que permite estimar la incertidumbre asociada a las predicciones y tomar decisiones más informadas. Esto es, proporcionan una media y una varianza para las respuestas del modelo. Esta tesis se centra en la aplicación de soluciones de aprendizaje automático basadas en procesos gaussianos a diversos problemas en robótica, con el objetivo de mejorar los métodos actuales y proporcionar una nueva perspectiva. Se abordan áreas clave como la planificación de trayectorias para vehículos aéreos no tripulados (UAVs), la planificación de movimientos para manipuladores robóticos y la identificación de modelos de sistemas no lineales. En el campo de la planificación de trayectorias para UAVs, se han desarrollado algoritmos basados en procesos gaussianos que permiten una planificación más eficiente y un ahorro de energía en misiones de exploración. Estos algoritmos se comparan con los enfoques analíticos tradicionales, demostrando su superioridad en términos de eficiencia al utilizar el aprendizaje automático. Se presentan algoritmos de recubrimiento de áreas y recubrimiento lineal con formaciones de UAVs, así como un algoritmo de búsqueda en superficies marinas. Finalmente, estos algoritmos se comparan con un nuevo método que utiliza procesos gaussianos para realizar predicciones probabilísticas y optimizar la planificación de trayectorias, lo que resulta en un rendimiento mejorado y una reducción del consumo de energía. En cuanto a la planificación de movimientos para manipuladores robóticos, se propone un enfoque basado en modelos gaussianos que permite una reducción significativa en los tiempos de cálculo. Se utiliza un modelo de procesos gaussianos para aproximar el espacio de configuraciones de un robot, lo que proporciona información valiosa para evitar colisiones y mejorar la seguridad en entornos dinámicos. Este enfoque se compara con los métodos convencionales de planificación de movimientos y se demuestra su eficacia en términos de tiempo de cálculo y precisión de los movimientos. En esta aplicación, la varianza proporciona información sobre zonas peligrosas para el manipulador. En cuanto a la identificación de modelos de sistemas no lineales, los procesos gaussianos también ofrecen ventajas significativas. Este enfoque se aplica a un sistema de brazo robótico blando y a modelos de consumo energético de UAVs, donde se utilizan datos experimentales para entrenar un modelo de proceso gaussiano que captura las relaciones entre las entradas y las salidas del sistema. Los resultados muestran una identificación precisa de los parámetros del sistema y la capacidad de realizar predicciones futuras confiables. En resumen, esta tesis presenta una variedad de aplicaciones de procesos gaussianos en robótica, desde la planificación de trayectorias y movimientos hasta la identificación de modelos. Estas soluciones basadas en aprendizaje automático ofrecen predicciones probabilísticas y mejoran la capacidad de los robots para realizar tareas de manera segura y eficiente. Los procesos gaussianos se posicionan como una herramienta poderosa para abordar los desafíos actuales en robótica y abrir nuevas posibilidades en el campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Juan Jesús Romero Cardalda.- Secretaria: María Dolores Blanco Rojas.- Vocal: Giuseppe Carbon

    Tactile mapping of harsh, constrained environments, with an application to oil wells

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. [110]-114).This work develops a practical approach to explore rough environments when time is critical. The harsh environmental conditions prevent the use of range, force/torque or tactile sensors. A representative case is the mapping of oil wells. In these conditions, tactile exploration is appealing. In this work, the environment is mapped tactilely, by a manipulator whose only sensors are joint encoders. The robot autonomously explores the environment collecting few, sparse tactile data and monitoring its free movements. These data are used to create a model of the surface in real time and to choose the robot's movements to reduce the mapping time. First, the approach is described and its feasibility demonstrated. Real-time impedance control allows a robust robot movement and the detection of the surface using a manipulator mounting only position sensors. A representation based on geometric primitives describes the surface using the few, sparse data available. The robustness of the method is tested against surface roughness and different surrounding fluids. Joint backlash strongly affect the robot's precision, and it is inevitable because of the thermal expansion in the joints. Here, a new strategy is developed to compensate for backlash positioning errors, by simultaneously identifying the surface and the backlash values. Second, an exploration strategy to map a constraining environment with a manipulator is developed. To maximize the use of the acquired data, this work proposes a hybrid approach involving both workspace and configuration space. The amount of knowledge of the environment is evaluated with an approach based on information theory, and the robot's movements are chosen to maximize the expected increase of such knowledge. Since the robot only possesses position sensors, the location along the robot where contact with the surface occurs cannot be determined with certainty. Thus a new approach is developed, that evaluates the probability of contact with specific parts of the robot and classifies and uses the data according to the different types of contact. This work is validated with simulations and experiments with a prototype manipulator specifically designed for this application.by Francesco Mazzini.Ph.D

    On Optimal Behavior Under Uncertainty in Humans and Robots

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    Despite significant progress in robotics and automation in the recent decades, there still remains a noticeable gap in performance compared to humans. Although the computation capabilities are growing every year, and are even projected to exceed the capacities of biological systems, the behaviors generated using current computational paradigms are arguably not catching up with the available resources. Why is that? It appears that we are still lacking some fundamental understanding of how living organisms are making decisions, and therefore we are unable to replicate intelligent behavior in artificial systems. Therefore, in this thesis, we attempted to develop a framework for modeling human and robot behavior based on statistical decision theory. Different features of this approach, such as risk-sensitivity, exploration, learning, control, were investigated in a number of publications. First, we considered the problem of learning new skills and developed a framework of entropic regularization of Markov decision processes (MDP). Utilizing a generalized concept of entropy, we were able to realize the trade-off between exploration and exploitation via a choice of a single scalar parameter determining the divergence function. Second, building on the theory of partially observable Markov decision process (POMDP), we proposed and validated a model of human ball catching behavior. Crucially, information seeking behavior was identified as a key feature enabling the modeling of observed human catches. Thus, entropy reduction was seen to play an important role in skillful human behavior. Third, having extracted the modeling principles from human behavior and having developed an information-theoretic framework for reinforcement learning, we studied the real-robot applications of the learning-based controllers in tactile-rich manipulation tasks. We investigated vision-based tactile sensors and the capability of learning algorithms to autonomously extract task-relevant features for manipulation tasks. The specific feature of tactile-based control that perception and action are tightly connected at the point of contact, enabled us to gather insights into the strengths and limitations of the statistical learning approach to real-time robotic manipulation. In conclusion, this thesis presents a series of investigations into the applicability of the statistical decision theory paradigm to modeling the behavior of humans and for synthesizing the behavior of robots. We conclude that a number of important features related to information processing can be represented and utilized in artificial systems for generating more intelligent behaviors. Nevertheless, these are only the first steps and we acknowledge that the road towards artificial general intelligence and skillful robotic applications will require more innovations and potentially transcendence of the probabilistic modeling paradigm
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