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The research of autonomous artificial agents that adapt to and survive in changing, possibly hostile environments, has gained momentum in recent years. Many of such agents incorporate mechanisms to learn and acquire new knowledge from its environment, a feature that becomes fundamental to enable the desired adaptation, and account for the challenges that the environment poses. The issue of how to trigger such learning, however, has not been as thoroughly studied as its significance suggest. The solution explored is based on the use of surprise (the reaction to unexpected events), as the mechanism that triggers learning. This thesis introduces a computational model of surprise that enables the robotic learner to experience surprise and start the acquisition of knowledge to explain it. A measure of surprise that combines elements from information and probability theory, is presented. Such measure offers a response to surprising situations faced by the robot, that is proportional to the degree of unexpectedness of such event. The concepts of short- and long-term memory are investigated as factors that influence the resulting surprise. Short-term memory enables the robot to habituate to new, repeated surprises, and to “forget” about old ones, allowing them to become surprising again. Long-term memory contains knowledge that is known a priori or that has been previously learned by the robot. Such knowledge influences the surprise mechanism, by applying a subsumption principle: if the available knowledge is able to explain the surprising event, suppress any trigger of surprise. The computational model of robotic surprise has been successfully applied to the domain of a robotic learner, specifically one that learns by experimentation. A brief introduction to the context of such application is provided, as well as a discussion on related issues like the relationship of the surprise mechanism with other components of the robot conceptual architecture, the challenges presented by the specific learning paradigm used, and other components of the motivational structure of the agent.
In service robotics, tasks without the involvement of objects are barely applicable, like in searching, fetching or delivering tasks. Service robots are supposed to capture efficiently object related information in real world scenes while for instance considering clutter and noise, and also being flexible and scalable to memorize a large set of objects. Besides object perception tasks like object recognition where the object’s identity is analyzed, object categorization is an important visual object perception cue that associates unknown object instances based on their e.g. appearance or shape to a corresponding category. We present a pipeline from the detection of object candidates in a domestic scene over the description to the final shape categorization of detected candidates. In order to detect object related information in cluttered domestic environments an object detection method is proposed that copes with multiple plane and object occurrences like in cluttered scenes with shelves. Further a surface reconstruction method based on Growing Neural Gas (GNG) in combination with a shape distribution-based descriptor is proposed to reflect shape characteristics of object candidates. Beneficial properties provided by the GNG such as smoothing and denoising effects support a stable description of the object candidates which also leads towards a more stable learning of categories. Based on the presented descriptor a dictionary approach combined with a supervised shape learner is presented to learn prediction models of shape categories.
Experimental results, of different shapes related to domestically appearing object shape categories such as cup, can, box, bottle, bowl, plate and ball, are shown. A classification accuracy of about 90% and a sequential execution time of lesser than two seconds for the categorization of an unknown object is achieved which proves the reasonableness of the proposed system design. Additional results are shown towards object tracking and false positive handling to enhance the robustness of the categorization. Also an initial approach towards incremental shape category learning is proposed that learns a new category based on the set of previously learned shape categories.
Das Optimalziel für ein Logistiklager ist eine hohe Auslastung des Transportsystems. Es stellt sich somit die Frage nach der Auswahl der Aufträge, die gleichzeitig innerhalb des Lagers abgearbeitet werden, ohne Staus, Blockaden oder Überlastungen entstehen zu lassen. Dieser Auswahlprozess wird auch als Path-Packing bezeichnet. Diese Masterthesis untersucht das Path-Packing auf graphentheoretischer Ebene und stellt verschiedene Greedy-Heuristiken, eine Optimallösung auf Basis der Linearen Programmierung sowie einen kombinierten Ansatz gegenüber. Die Ansätze werden anhand von Messzeiten und Auslastungen unterschiedlich randomisiert erstellter Testdaten ausgewertet.
Interactive Object Detection
(2019)
The success of state-of-the-art object detection methods depend heavily on the availability of a large amount of annotated image data. The raw image data available from various sources are abundant but non-annotated. Annotating image data is often costly, time-consuming or needs expert help. In this work, a new paradigm of learning called Active Learning is explored which uses user interaction to obtain annotations for a subset of the dataset. The goal of active learning is to achieve superior object detection performance with images that are annotated on demand. To realize active learning method, the trade-off between the effort to annotate (annotation cost) unlabeled data and the performance of object detection model is minimised.
Random Forests based method called Hough Forest is chosen as the object detection model and the annotation cost is calculated as the predicted false positive and false negative rate. The framework is successfully evaluated on two Computer Vision benchmark and two Carl Zeiss custom datasets. Also, an evaluation of RGB, HoG and Deep features for the task is presented.
Experimental results show that using Deep features with Hough Forest achieves the maximum performance. By employing Active Learning, it is demonstrated that performance comparable to the fully supervised setting can be achieved by annotating just 2.5% of the images. To this end, an annotation tool is developed for user interaction during Active Learning.
The ability to finely segment different instances of various objects in an environment forms a critical tool in the perception tool-box of any autonomous agent. Traditionally instance segmentation is treated as a multi-label pixel-wise classification problem. This formulation has resulted in networks that are capable of producing high-quality instance masks but are extremely slow for real-world usage, especially on platforms with limited computational capabilities. This thesis investigates an alternate regression-based formulation of instance segmentation to achieve a good trade-off between mask precision and run-time. Particularly the instance masks are parameterized and a CNN is trained to regress to these parameters, analogous to bounding box regression performed by an object detection network.
In this investigation, the instance segmentation masks in the Cityscape dataset are approximated using irregular octagons and an existing object detector network (i.e., SqueezeDet) is modified to regresses to the parameters of these octagonal approximations. The resulting network is referred to as SqueezeDetOcta. At the image boundaries, object instances are only partially visible. Due to the convolutional nature of most object detection networks, special handling of the boundary adhering object instances is warranted. However, the current object detection techniques seem to be unaffected by this and handle all the object instances alike. To this end, this work proposes selectively learning only partial, untainted parameters of the bounding box approximation of the boundary adhering object instances. Anchor-based object detection networks like SqueezeDet and YOLOv2 have a discrepancy between the ground-truth encoding/decoding scheme and the coordinate space used for clustering, to generate the prior anchor shapes. To resolve this disagreement, this work proposes clustering in a space defined by two coordinate axes representing the natural log transformations of the width and height of the ground-truth bounding boxes.
When both SqueezeDet and SqueezeDetOcta were trained from scratch, SqueezeDetOcta lagged behind the SqueezeDet network by a massive ≈ 6.19 mAP. Further analysis revealed that the sparsity of the annotated data was the reason for this lackluster performance of the SqueezeDetOcta network. To mitigate this issue transfer-learning was used to fine-tune the SqueezeDetOcta network starting from the trained weights of the SqueezeDet network. When all the layers of the SqueezeDetOcta were fine-tuned, it outperformed the SqueezeDet network paired with logarithmically extracted anchors by ≈ 0.77 mAP. In addition to this, the forward pass latencies of both SqueezeDet and SqueezeDetOcta are close to ≈ 19ms. Boundary adhesion considerations, during training, resulted in an improvement of ≈ 2.62 mAP of the baseline SqueezeDet network. A SqueezeDet network paired with logarithmically extracted anchors improved the performance of the baseline SqueezeDet network by ≈ 1.85 mAP.
In summary, this work demonstrates that if given sufficient fine instance annotated data, an existing object detection network can be modified to predict much finer approximations (i.e., irregular octagons) of the instance annotations, whilst having the same forward pass latency as that of the bounding box predicting network. The results justify the merits of logarithmically extracted anchors to boost the performance of any anchor-based object detection network. The results also showed that the special handling of image boundary adhering object instances produces more performant object detectors.
Object detectors have improved considerably in the last years by using advanced Convolutional Neural Networks (CNNs) architectures. However, many detector hyper-parameters are not generally tuned, and they are used with values set by the detector authors. Blackbox optimization methods have gained more attention in recent years because of its ability to optimize the hyper-parameters of various machine learning algorithms and deep learning models. However, these methods are not explored in improving CNN-based object detector's hyper-parameters. In this research work, we propose the use of blackbox optimization methods such as Gaussian Process based Bayesian Optimization (BOGP), Sequential Model-based Algorithm Configuration (SMAC), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to tune the hyper-parameters in Faster R-CNN and Single Shot MultiBox Detector (SSD). In Faster R-CNN, tuning the input image size, prior box anchor scales and ratios using BOGP, SMAC, and CMA-ES has increased the performance around 1.5% in terms of Mean Average Precision (mAP) on PASCAL VOC. Tuning the anchor scales of SSD has increased the mAP by 3% on PASCAL VOC and marine debris datasets. On the COCO dataset with SSD, mAP improvement is observed in the medium and large objects, but mAP decreases by 1% in small objects. The experimental results show that the blackbox optimization methods have proved to increase the mAP performance by optimizing the object detectors. Moreover, it has achieved better results than the hand-tuned configurations in most of the cases.
Diese Arbeit beschäftigt sich mit der Entwicklung eines, für die kontrollierte Freisetzung hydrophiler Wirkstoffe geeigneten, Verkapselungssystems mit dem Ziel die Freisetzung osteospezifischer P2-Liganden zu verzögern, um bei der Behandlung von Knochendefekten kritischer Größe die Bildung neuen Knochengewebes zu gewährleisten. Hierfür werden, unter Anwendung der immersiven Layer-by-Layer-Beschichtung, mit den Modell-Substanzen Adenosintriphosphat und Suramin versetzte, Alginat sowie κ-Carrageen-Kapseln mit Chitosan und Lignosulfonat beschichtet und auf ihr Freisetzungsverhalten hin untersucht.
In dieser vorliegenden Arbeit wurde der photolytische und photokatalytische Abbau von Lignin untersucht. Eine Charakterisierung des verwendeten Photoreaktors wurde mittels Kalium-Ferrioxalat-Aktinometrie durchgeführt. Zur Analyse der abgebauten Lignine wurde eine Optimierung einer bereits bestehenden Methode zur Bestimmung des Hydroxylgehaltes erarbeitet. Die Bestimmung der Hydroxylgehalte erfolgte demnach bei Raumtemperatur nach einer Acetylierungsdauer von 72 h und zeigte eine Abnahme der Hydroxylgehalte mit andauernder UV-Bestrahlung. Selbige Beobachtung konnte mit Hilfe der ATR-IR-Spektroskopie gemacht werden. Zusätzlich konnte die Bildung von Carbonsäuren und der Abbau von aromatischen Strukturen detektiert werden. Der Abbau aromatischer Strukturen konnte ebenfalls durch UV-VIS-Spektroskopie gezeigt werden. Eine Vermutung, dass es sich bei dem Abbauprozess um einen oxidativen Mechanismus handelt, konnte mit dem Abbau von Hydroxylgruppen über eine Bildung von Carbonsäuren zu Kohlenstoffdioxid bestätigt werden. Eine Freisetzung von Kohlenstoffdioxid konnte durch eine Bestimmung des IC festgestellt werden. Die Ergebnisse der Gel-Permeations-Chromatographie zusammen mit einer TOC-Analyse zeigen einen Abbau der molaren Masse des Lignins auf. Es konnten Fragmente mit einer Molmasse ähnlich der Monomere des Lignins gefunden werden. Der eingesetzte Photokatalysator wurde via Röntgenbeugung untersucht und konnte als das hoch photokatalytisch aktive P25 von Degussa identifiziert werden. Trotz des Einsatzes verschiedener Katalysatorkonzentrationen in einem Bereich von 0-0,5 g L^(-1) konnte kein Einfluss des Photokatalysators auf den Abbauprozess des Lignins beobachtet werden.
Die vorliegende Studie untersucht als Erste simultan die Auswirkungen des dreidimensionalen Konstrukts der prozeduralen, distributiven und kommunikativen Lohntransparenz auf Arbeitnehmer, auch unter Berücksichtigung von persönlichen Einstellungen und dem tatsächlichen Gehalt anhand einer deutschen Stichprobe (N = 159). Hierfür wurden Angestellte in einer querschnittlichen Online-Fragebogenstudie zu der wahrgenommenen Lohntransparenz in ihrer Organisation sowie zu weiteren arbeitnehmer- und organisationsrelevanten Variablen befragt. Mittels regressionsanalytischer Untersuchungen konnten hypothesenkonform positive Zusammenhänge der Lohntransparenz mit der Lohnzufriedenheit, der Wahrnehmung prozeduraler und distributiver Gerechtigkeit sowie mit dem Empfinden organisationalen Vertrauens nachgewiesen werden. Von wesentlicher Bedeutung für die Zusammenhänge war allerdings lediglich die prozedurale Lohntransparenz als eine der drei Dimensionen. Weiterhin ergaben Moderatoranalysen, dass ein geringes Bedürfnis nach informationeller Privatheit sowie ein geringes Bruttoentgelt die positiven Zusammenhänge der Lohntransparenz mit den Kriteriumsvariablen partiell verstärken. Abschließend werden Implikationen der Befunde für die Forschung und Praxis vor dem Hintergrund der Einschränkungen, denen diese Studie unterliegt, erläutert.