Searched for: contributor:"Kober, Jens (mentor)"
(1 - 15 of 15)
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Jacobs, Olav (author)
In this thesis, a control scheme for lifting parcels using two robot manipulators is presented. The robots do not have a rigid grasp on the object. Instead, they use friction to lift the parcels. First, a controller calculates the desired force to make sure the parcels do not slip. The required force, as well as a trajectory are then sent to a...
master thesis 2020
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Jauhri, Snehal (author)
Imitation Learning is a technique that enables programming the behavior of agents through demonstration, as opposed to manually engineering behavior. However, Imitation Learning methods require demonstration data (in the form of state-action labels) and in many scenarios, the demonstrations can be expensive to obtain or too complex for a...
master thesis 2020
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de Zwart, Sjouke (author)
We often establish contact with our environment at non-zero speed. Grabbing and pushing objects without the need to stop our hands at the moment of impact is an example of this. Although humans learn and execute such tasks with relative ease, robots cannot. The difficulty in executing such tasks lies in the complexity of control at the moment of...
master thesis 2019
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Scholten, Jan (author)
Deep Reinforcement Learning enables us to control increasingly complex and high-dimensional problems. Modelling and control design is longer required, which paves the way to numerous in- novations, such as optimal control of evermore sophisticated robotic systems, fast and efficient scheduling and logistics, effective personal drug dosing...
master thesis 2019
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Wout, Daan (author)
A prevalent approach for learning a control policy in the model-free domain is by engaging Reinforcement Learning (RL). A well known disadvantage of RL is the necessity for extensive amounts of data for a suitable control policy. For systems that concern physical application, acquiring this vast amount of data might take an extraordinary amount...
master thesis 2019
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Beeftink, Mart (author)
To successfully perform manipulation tasks in an unknown environment, a robot must be able to learn the kinematic constraints of the objects within this environment. Over the years, many studies have investigated the possibility to learn the kinematic models of articulated objects using a Learning from Demonstration (LfD) approach. In the...
master thesis 2018
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Krishnamoorthi, Sathish (author)
Industrial robots can be found in automotive, food, chemical, and electronics industries. These robots are often caged and are secluded from human beings. A recent trend in a subclass of industrial robots named collaborative robots allows the humans to interact with the robots safely. The word “safety” mentioned above is of supreme importance....
master thesis 2018
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Kist, Martijn (author)
Pioneering Spirit, Allseas's largest pipelay vessel, will be outfitted with a novel Jacket Lift System (JLS). A jacket refers to the steel frame which supports the topside of a fixed offshore platform. The Pioneering Spirit was already capable of lifting the topsides of offshore platforms, like the former oil platform Brent Delta, but would have...
master thesis 2018
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Kokkalis, Konstantinos (author)
Learning capabilities are a key requisite for an autonomous agent operating in dynamically changing and complex environments, where pre-programming is not anymore possible. Furthermore, it is essential to guarantee that the learning agent will act safely by considering its stability properties. In this thesis, novel conditions are proposed,...
master thesis 2018
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Keulen, Bart (author)
An important problem in reinforcement learning is the exploration-exploitation dilemma. Especially for environments with sparse or misleading rewards it has proven difficult to construct a good exploration strategy. For discrete domains good exploration strategies have been devised, but are often nontrivial to implement on more complex domains...
master thesis 2018
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Duan, Wuyang (author)
Representation learning is a central topic in the field of deep learning. It aims at extracting useful state representations directly from raw data. In deep learning, state representations are usually used for classification or inferences. For example, image embedding that provides similarity metrics can be used for face recognition. Recent...
master thesis 2017
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Radojević, Jovana (author)
A lot of attention has recently been focused on possible benefits of the cooperation between machines and humans. Taking the best from machines and humans and joining them together can produce results which exceed each collaborating partner performing separately. A common belief is that the<br/>key for good cooperation is an excellent...
master thesis 2017
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Guljelmović, Nikol (author)
Task-parameterized movement representation, as an approach for the generalization of demonstrations, is used to represent data from multiple local perspectives within the global reference frame, through which more accurate information about multiple aspects of the movement is given. The estimated transformation between the different perspectives...
master thesis 2017
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Ravi, Siddharth (author)
This project addresses a fundamental problem faced by many reinforcement learning agents. Commonly used reinforcement learning agents can be seen to have deteriorating performances at increasing frequencies, as they are unable to correctly learn the ordering of expected returns for actions that are applied. We call this the disappearing...
master thesis 2017
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Rastogi, Divyam (author)
Reinforcement Learning (RL) is a general purpose framework for designing controllers for non-linear systems. It tries to learn a controller (policy) by trial and error. This makes it highly suitable for systems which are difficult to control using conventional control methodologies, such as walking robots. Traditionally, RL has only been...
master thesis 2017
Searched for: contributor:"Kober, Jens (mentor)"
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