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Mészáros, Anna (author)
Grasping objects in a smooth humanlike motion, instead of the more typical pick-and-place approach, includes multiple aspects that need to be performed correctly for a successful grasp. These aspects involve moving the end-effector such that its surface makes and retains contact with the object while also coordinating the movement of the gripper...
master thesis 2021
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Lander, Niels (author)
To generalize the use of robotics, there are a few hurdles still to take. One of these hurdles is the programming of the robots. Most robots on the market today employ position control, with a set of controller parameters tuned by an expert. This programming is quite expensive, only suited for a single task, in a single configuration, and not...
master thesis 2021
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Hammudoğlu, Joren (author)
Recommender systems are essential for filtering immense amounts of available digital content. As these quantities keep increasing, the impact of recommendations does so as well. In this work, we address negative impacts current state-of-the-art recommenders have. For the algorithmic filtering of items that are recommended to users, collaborative...
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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