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Autonomous Motion Intelligent Control Systems Master Thesis Gaussian Process Optimization for Self-Tuning Control Marco, A. Polytechnic University of Catalonia (BarcelonaTech), October 2015 PDF BibTeX
Autonomous Motion Intelligent Control Systems Master Thesis Adaptive and Learning Concepts in Hydraulic Force Control Doerr, A. University of Stuttgart, September 2015 BibTeX
Autonomous Motion Master Thesis Object Detection Using Deep Learning - Learning where to search using visual attention Kloss, A. Eberhard Karls Universität Tübingen, May 2015
Detecting and identifying the different objects in an image fast and reliably is an important skill for interacting with one’s environment. The main problem is that in theory, all parts of an image have to be searched for objects on many different scales to make sure that no object instance is missed. It however takes considerable time and effort to actually classify the content of a given image region and both time and computational capacities that an agent can spend on classification are limited. Humans use a process called visual attention to quickly decide which locations of an image need to be processed in detail and which can be ignored. This allows us to deal with the huge amount of visual information and to employ the capacities of our visual system efficiently. For computer vision, researchers have to deal with exactly the same problems, so learning from the behaviour of humans provides a promising way to improve existing algorithms. In the presented master’s thesis, a model is trained with eye tracking data recorded from 15 participants that were asked to search images for objects from three different categories. It uses a deep convolutional neural network to extract features from the input image that are then combined to form a saliency map. This map provides information about which image regions are interesting when searching for the given target object and can thus be used to reduce the parts of the image that have to be processed in detail. The method is based on a recent publication of Kümmerer et al., but in contrast to the original method that computes general, task independent saliency, the presented model is supposed to respond differently when searching for different target categories.
PDF BibTeX
Autonomous Motion Master Thesis Robot Arm Tracking with Random Decision Forests Widmaier, F. Eberhard-Karls-Universität Tübingen, May 2015
For grasping and manipulation with robot arms, knowing the current pose of the arm is crucial for successful controlling its motion. Often, pose estimations can be acquired from encoders inside the arm, but they can have significant inaccuracy which makes the use of additional techniques necessary. In this master thesis, a novel approach of robot arm pose estimation is presented, that works on single depth images without the need of prior foreground segmentation or other preprocessing steps. A random regression forest is used, which is trained only on synthetically generated data. The approach improves former work by Bohg et al. by considerably reducing the computational effort both at training and test time. The forest in the new method directly estimates the desired joint angles while in the former approach, the forest casts 3D position votes for the joints, which then have to be clustered and fed into an iterative inverse kinematic process to finally get the joint angles. To improve the estimation accuracy, the standard training objective of the forest training is replaced by a specialized function that makes use of a model-dependent distance metric, called DISP. Experimental results show that the specialized objective indeed improves pose estimation and it is shown that the method, despite of being trained on synthetic data only, is able to provide reasonable estimations for real data at test time.
PDF BibTeX
Autonomous Motion Master Thesis Pole Balancing with Apollo Kaden, H. Eberhard Karls Universität Tübingen, December 2014 BibTeX
Autonomous Motion Master Thesis Learning Coupling Terms for Obstacle Avoidance Rai, A. École polytechnique fédérale de Lausanne, August 2014 BibTeX
Autonomous Motion Master Thesis Object Tracking in Depth Images Using Sigma Point Kalman Filters Issac, J. Karlsruhe Institute of Technology, July 2014 BibTeX
Autonomous Motion Master Thesis Probabilistic Object Tracking on the GPU Pfreundt, C. Karlsruhe Institute of Technology, March 2014 Probabilistic Object Tracking on the GPU BibTeX
Autonomous Motion Master Thesis Learning of grasp selection based on shape-templates Herzog, A. Karlsruhe Institute of Technology, 2011 BibTeX