Searched for: author%3A%22Derner%2C+Erik%22
(1 - 12 of 12)
document
Kubalik, Jiri (author), Derner, Erik (author), Babuska, R. (author)
Many real-world systems can be described by mathematical models that are human-comprehensible, easy to analyze and help explain the system's behavior. Symbolic regression is a method that can automatically generate such models from data. Historically, symbolic regression has been predominantly realized by genetic programming, a method that...
journal article 2023
document
Kulhánek, Jonáš (author), Derner, Erik (author), Sattler, Torsten (author), Babuska, R. (author)
Novel view synthesis is a long-standing problem. In this work, we consider a variant of the problem where we are given only a few context views sparsely covering a scene or an object. The goal is to predict novel viewpoints in the scene, which requires learning priors. The current state of the art is based on Neural Radiance Field (NeRF), and...
conference paper 2022
document
Kulhanek, Jonas (author), Derner, Erik (author), Babuska, R. (author)
Visual navigation is essential for many applications in robotics, from manipulation, through mobile robotics to automated driving. Deep reinforcement learning (DRL) provides an elegant map-free approach integrating image processing, localization, and planning in one module, which can be trained and therefore optimized for a given environment....
journal article 2021
document
Kubalik, Jiri (author), Derner, Erik (author), Zegklitz, Jan (author), Babuska, R. (author)
Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as neural networks or basis...
journal article 2021
document
Kubalík, Jiří (author), Derner, Erik (author), Babuska, R. (author)
Virtually all dynamic system control methods benefit from the availability of an accurate mathematical model of the system. This includes also methods like reinforcement learning, which can be vastly sped up and made safer by using a dynamic system model. However, obtaining a sufficient amount of informative data for constructing dynamic...
journal article 2021
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Derner, Erik (author), Gomez, Clara (author), Hernandez, Alejandra C. (author), Barber, Ramon (author), Babuska, R. (author)
Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled to achieve long-term robot autonomy. In vision-based localization and navigation methods, one of the major issues is the scene dynamics. The autonomous...
journal article 2021
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Derner, Erik (author), Kubalik, Jiri (author), Babuska, R. (author)
Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy is impaired when data from repetitive motions prevail in the training set and outweigh...
journal article 2021
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Derner, Erik (author), Kubalík, Jiří (author), Ancona, N. (author), Babuska, R. (author)
Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system's behavior, decision making and design of automatic control algorithms. Even inherently model-free control techniques such as reinforcement learning (RL) have been shown to benefit...
journal article 2020
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Gomez, Clara (author), Hernandez, Alejandra C. (author), Derner, Erik (author), Barber, Ramon (author), Babuska, R. (author)
Relying on static representations of the environment limits the use of mapping methods in most real-world tasks. Real-world environments are dynamic and undergo changes that need to be handled through map adaptation. In this work, an object-based pose graph is proposed to solve the problem of mapping in indoor dynamic environments with mobile...
journal article 2020
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Kubalik, Jiai (author), Derner, Erik (author), Babuska, R. (author)
In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models. Standard symbolic regression techniques then...
conference paper 2020
document
Derner, Erik (author), Gomez, Clara (author), Hernandez, Alejandra C. (author), Barber, Ramon (author), Babuska, R. (author)
Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled in order to move closer to the ultimate goal of life-long autonomy. In computer vision-based methods employed on mobile robots, such as localization or...
conference paper 2019
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Kulhánek, J. (author), Derner, Erik (author), de Bruin, T.D. (author), Babuska, R. (author)
Deep reinforcement learning (RL) has been successfully applied to a variety of game-like environments. However, the application of deep RL to visual navigation with realistic environments is a challenging task. We propose a novel learning architecture capable of navigating an agent, e.g. a mobile robot, to a target given by an image. To...
conference paper 2019
Searched for: author%3A%22Derner%2C+Erik%22
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