Bayesian Optimization is a powerful method to optimize black-box derivative-free functions, with high evaluation costs. For instance, applications can be found in the context of robotics, animation design or molecular design. However, Bayesian Optimization is not able to scale into higher dimensions, equivalent to optimizing more than 20 parameters. This thesis introduces HIBO, a new hierarchical algorithm in the context of high dimensional Bayesian Optimization. The algorithm uses an automatic feature generation. The features are used to condition the parameters, to enable faster optimization. The performance of HIBO is compared to existing high dimensional extensions of Bayesian Optimization on three common benchmark functions. Additionally, an air hockey simulation is used to examine the capability in a task-oriented setting. The conducted experiments show that HIBO performs similar to the basic Bayesian Optimization algorithm, independent from the dimensionality of the given problem. Hence, the proposed HIBO algorithm does not scale Bayesian Optimization to higher dimensions.

In this master thesis, a prototype is being developed that uses existing frameworks to make a Smartphone to a pocket 3D scanner. The resulting prototype consists of a smartphone-, server-application, and a database in between. On the server application, point clouds from photos are created in real time. The point clouds and user support, should be used to assist during the photo creation and reconstruction of 3D-models from photos. After an introductory consideration of the theoretical background, common algorithms of 3D reconstruction are examined for their advantages and disadvantages with regard to the computing time and efficiency with the target platform. Based on the results, a combination of MeshLab, OpenMVG, OpenMVE, and SMVS is used to construct the 3D models. On the basis of fault tolerance with unsorted images, AKAZE and DAISY have been selected, which is why they are being supplemented in the OpenMVG application in this work. This work answers the question of How a technological can be implemente.

Based on the intention to build an autonomous lawn mower robot, this work examines the viability of a sensor and microprocessor for onboard plant classification using machine learning. Usually, some sort of fencing is required to keep the robot in its intended processing area, so such a sensor would allow the robot to differentiate between grass and e.g. flowers. Also, the drive and blade speed can be adjusted for certain species or plant densities, etc.
For this, a data set was collected utilizing a specific method called chlorophyll fluorescence induction. A series of narrowband LEDs are used to drive this process, while a spectrometer measures the spectral intensity. The plant will fluoresce in specific wavelengths when sufficiantly illuminated. With this data, machine learning algorithms are trained to explore if they are capable to classify these plants without further information.
With accuracies up to 98% for three plants commonly present on a lawn and up to 86% for eight plants, the results show that chlorophyll fluorescence is a viable method for classification, even under sunlight using the random forest machine learning algorithm

Preparative high-performance liquid chromatography is a chemical procedure in which a components mixture is separated into its components. In this procedure, fraction collectors are used to fill the separated components of a mixture into their dedicated vials. Fraction collectors available on the marked are usually made of a multiplicity of custom-made parts, which results in high manufacturing costs. A wide used drive concept for fraction collectors is the spindle drive, which is slow due to its gear ration. In this work, we propose an approach of building a fraction collector using a fast timing belt drive, which has performed well for 3D-Printers in practice. The result of a first prototype showed, that this drive-concept can be adapted also for fraction collectors. Thereby, the material and manufacturing costs can be kept low using a manufacturing aware part design.

As an versatile, fast and innocuous imaging technique, medical ultrasound is wellestablished in clinical practise. In comparison to other imaging techniques, diagnostic success is highly user-dependent. Due to limited access to qualified medical personnel, the potential of medical ultrasound has not been exploited fully. Over the last three decades, this problem has been addressed by several research groups, that established the field of robotic ultrasound imaging. Although numerous findings have been made, to this day there is no robotic system fit for widespread clinical use. A significant reason for this has been an insuficcient kinematic design. Therefore kinematic optimization is one of the main tasks in the field of robotic ultrasound imaging. In this thesis we present a simulation framework, that can be utilized to explore the fitness of a certain kinematic structure concerning ultrasound imaging

We propose and investigate a novel type of parameterized policy based on a two-layered stochastic spiking neural network consisting of multiple populations of stochastic spiking neurons. We show that the proposed policy type is a spatially distributed generalization of a discrete basic policy with lookup table parameterization. Our policy reveals remarkable capabilities but also crucial limitations. In particular, our policy is able to deal with high-dimensional state and action spaces but loses expressive power such that it cannot represent certain functions like XOR.
Furthermore, we propose corresponding reinforcement learning methods to train the policy. These methods are based on value function methods and generalize these to train our distributed policy type. We compare these methods to state-ofthe-art methods including black-box approaches and likelihood ratio approaches. It turns out that our proposed methods outperform these methods significantly. In our experiments we demonstrate that our policy can be trained effectively from rewards to guide a 10-link robot-arm in a toy task through a grid world to reach a specified target without hitting obstacles.

In Genetic programming, the genetic operators mutation and crossover are defined by the developer in order to produce a system that automatically generates computer code, usually incorporating information about the target language as well as about the problem. The genetic operators are a critical design decision because they control the evolution of genomes. This thesis outlines a way of eliminating that design decision by parameterizing genetic operators as part of the genome. In doing so, we discovered that several measures are necessary to accommodate the lack of assumptions that can be made about genomes. Among these measures are the introduction of an ancestry tree of genomes as additional bookkeeping to enable backtracking, the employment of a method for selection of candidates from that structure and the manual implementation of the first genome.
In our experiments, we discovered that -when uncompensated for- our approach will tend to bloat the genome extremely and distort our metrics. The cause of this was that individuals changed their own code to better fit the way their code generator works. We were able to solve this though, by employing only crossover, thus shifting the responsibility of improvement solely on the code generator’s behavior. Our variant of crossover passes one genome’s code to another genomes mutation function. We also discovered that the exact implementation of the initial genome is extremely important and seemingly inconsequential changes like removing a few lines of dead code make a huge difference. The results show that our current setup is still insufficient but demonstrates that our measures are effective.

The Sake Robotics Gripper is a cheap, robust and versatile gripper that has not been simulated yet. It is an underactuated gripper and therefore needs a workaround to achieve realistic behavior in simulation. The model has to be able to interpret the exact same ROS messages the gripper receives. This paper proposes a reproduction of the Sake Robotics Gripper in V-REP. We analyze the tools provided by V-REP to develop an algorithm to achieve underactuation in simulation. This contributes to the kitchen cleaning task of the GOAL-Robots project with a platform to do machine learning on. All models are available open source. Our model can be used as a foundation for research in complex grasping and manipulation tasks with the Sake Robotics Gripper

A bug report plays an essential role in software development and maintenance. It is a key element of an efficient IT problem management. Due to the complexity of a software product, many teams such as design, usability, performance etc. work on a single product. Bug reports typically need to be routed to these different expert teams to be resolved. A manual routing process is time-consuming and less efficient. Therefore, the challenge is to find an approach that automate the routing process. With the advent of natural language processing methods, machine learning and deep learning algorithms, new automatic approaches were proposed to address this challenge.
In this thesis, bug reports from the business process manager (BPM) product of IBM Deutschland GmbH were used to compare and assess the performance of prediction models. Different combination of natural language processing methods (i.e., lemmatization, pos tagger, N-gram and stopword removal) and classification algorithms (i.e., linear support vector machines (SVM), multinomial naive Bayes, and long sort term memory (LSTM)) were evaluated. The feature processing was done using the term frequency-inverse document frequency (TF-IDF) method. Our evaluation shows that the best obtained prediction model for bug triage system is a combination of the bigram method with a linear support vector machine. For the long short term memory (LSTM), more than 8000 features would be needed. The bug triage tool wasimplemented in microservice architecture using docker containers which allows for extending of individual componentsof frameworks.

Robots are an essential part of our industrial production process and in the future robots might accompany us every day.

In all this we need the computing of movement trajectories to solve a task, including considering constraints. This task solving is condensed in the term planning and thus planning is a fundamental knowledge in nearly all robotic tasks. For this application Spiking Neural Networks (SNN) are one of the new state of the art learning algorithms. SNNs incorporate the concept of time, they also include information to be processed asynchrony, event-based and without delay.

SNN’s are uniquely equipped to solve planning problems. This demands an increase of synapses, which is (going to be) compensated by neuromorphic hardware. The number of synapses is significantly higher, this also reflects on the increased consumption. In this work, we want to show that models for synapse pruning decrease the synapses needed and in addition result in a significant decrease of memory consumption.