Searched for: author%3A%22Babuska%2C+R.%22
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Vastl, Martin (author), Kulhanek, Jonas (author), Kubalik, Jiri (author), Derner, Erik (author), Babuska, R. (author)
Many real-world systems can be naturally described by mathematical formulas. The task of automatically constructing formulas to fit observed data is called symbolic regression. Evolutionary methods such as genetic programming have been commonly used to solve symbolic regression tasks, but they have significant drawbacks, such as high...
journal article 2024
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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
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Sedlar, Jiri (author), Stepanova, Karla (author), Skoviera, Radoslav (author), Behrens, Jan K. (author), Tuna, Matus (author), Sejnova, Gabriela (author), Sivic, Josef (author), Babuska, R. (author)
This letter introduces a dataset for training and evaluating methods for 6D pose estimation of hand-held tools in task demonstrations captured by a standard RGB camera. Despite the significant progress of 6D pose estimation methods, their performance is usually limited for heavily occluded objects, which is a common case in imitation learning...
journal article 2023
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Passalis, N. (author), Pedrazzi, S. (author), Babuska, R. (author), Burgard, W. (author), Ferro, F. (author), Gabbouj, M. (author), Kayacan, E. (author), Kober, J. (author), Pieters, RRGM (author), Valada, A. (author)
Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which...
conference paper 2022
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Lodel, M. (author), Ferreira de Brito, B.F. (author), Serra Gomez, A. (author), Ferranti, L. (author), Babuska, R. (author), Alonso-Mora, J. (author)
Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current methods for information gathering, such as Monte Carlo Tree Search, are capable of reasoning over long horizons, but they are computationally expensive. An alternative...
conference paper 2022
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Kocijan, Jus (author), Babuska, R. (author), Guelton, Kevin (author), Lendek, Zsófia (author), Busoniu, Lucían (author)
contribution to periodical 2022
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Baaij, T.A. (author), Klein Holkenborg, Marn (author), Stölzle, Maximilian (author), van der Tuin, Daan (author), Naaktgeboren, Jonatan (author), Babuska, R. (author), Della Santina, C. (author)
Sensing the shape of continuum soft robots without obstructing their movements and modifying their natural softness requires innovative solutions. This letter proposes to use magnetic sensors fully integrated into the robot to achieve proprioception. Magnetic sensors are compact, sensitive, and easy to integrate into a soft robot. We also...
journal article 2022
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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
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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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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
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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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van der Heijden, D.S. (author), Ferranti, L. (author), Kober, J. (author), Babuska, R. (author)
This paper presents DeepKoCo, a novel modelbased agent that learns a latent Koopman representation from images. This representation allows DeepKoCo to plan efficiently using linear control methods, such as linear model predictive control. Compared to traditional agents, DeepKoCo learns taskrelevant dynamics, thanks to the use of a tailored lossy...
conference paper 2021
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Brablc, Martin (author), Žegklitz, Jan (author), Grepl, Robert (author), Babuska, R. (author)
Reinforcement learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural...
journal article 2021
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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
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Mazhar, O. (author), Babuska, R. (author), Kober, J. (author)
Deep neural networks designed for vision tasks are often prone to failure when they encounter environmental conditions not covered by the training data. Single-modal strategies are insufficient when the sensor fails to acquire information due to malfunction or its design limitations. Multi-sensor configurations are known to provide redundancy...
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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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
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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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de Bruin, T.D. (author), Kober, J. (author), Tuyls, Karl (author), Babuska, R. (author)
Deep reinforcement learning makes it possible to train control policies that map high-dimensional observations to actions. These methods typically use gradient-based optimization techniques to enable relatively efficient learning, but are notoriously sensitive to hyperparameter choices and do not have good convergence properties. Gradient...
journal article 2020
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