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Professorship Artificial Intelligence

Research Seminar

The seminar is intended for interested students in their master or final stages of bachelor studies. However, other interested listeners are cordially welcomed too! In the seminar, either the staff of the professorship Artificial Intelligence or students will present current research topics. The presentations are normally hold in English. The seminar will be hold irregularly in the room 1/336 whereby the precise schedule is presented in the following table.

Information regarding diploma and master students

Seminar presentations of each major could also be hold in the frame of this event. This includes mainly the seminar “Hauptseminar" in the major Diplom-IF/AIF or the "Forschungsseminar" in all master majors of Informatik. Both courses have the goal that the attendees acquire research-oriented knowledge by themself and present it during talks. The research topics will be typically from the field of Artificial Intelligence with emphasis on object recognition, neuro-computing at graphic cards or multi-core-CPUs, reinforcement learning and intelligent agents in virtual reality. However, other topics could also be chosen. Interested students should write an Email to Prof. Hamker, the talk itself will be scheduled after an individual consultation.

Next Events

Weight estimation using sensory soft pneumatic gripper

Hardik Sagani

Mon, 25. 3. 2019, 11:30, Room TBA

Current soft pneumatic grippers might robustly grasp flat and flexible objects with curved surfaces without distorting them, but not estimate the properties by manipulating it to a certain extent. On the other hand, it is difficult to actively deform a complex gripper to pick up surface objects. Hence, this research project represents a prototype design of soft sensory pneumatic gripper utilized to estimate the weight of objects. An easy to implement soft gripper is proposed by soft silicon material with four fingers. It can be fabricated by placing piezoresistive material sandwiched between soft silicon rubber and conducting threads. Sixteen Pressure sensors and 4 curvature sensors of velostat piezoresistive material have been placed into the hand. The layers were designed with the help of conductive thread electrode material. Hence, the sensory gripper can evaluate the weight property of grasped objects.

Recent Events

Modelling and controlling of the soft pneumatic actuator for the Elbow assistance

Sidhdharthkumar Vaghani

Tue, 12. 3. 2019, Room 132

More than a decade the wearable robotics is in the trend for the rehabilitation or for the assistance of human motion. The wearable robots are attracting the focus of the human because of its safe interaction with the human and external environment. Because of their stiffness and wearable property, wearable robots are very famous for the rehabilitation and assistance of human body joints e.g. knee rehabilitation, elbow or shoulder assistance etc. However the controlling of the soft robots is not an easy task compared to rigid robots. Mainly the wearable robots are using the air pressure as the activation parameter of their body. Therefore the aim of this thesis is to modelling and controlling of the exosuit, which is useful for the elbow assistance. The structure of the exosuit consists of two pneumatic actuator one is for elbow flexion movement and other is for extension movements. One IMU unit is mounted on the top of the exosuit to monitor the relative angle between two links of the exosuit. The modelling of the exosuit is carried out separately for flexion and extension actuator by considering the geometry of the actuators. For precise control of the actuator proportional valve has been developed and used along with the PID controller. The multi-layer multi-pattern central pattern generator (MLMP-CPG) is adapted to generate the rhythmic and non-rhythmic behaviour of the exosuit. To implement a better control strategy, the reflexes are introduced with the CPG model. At the end of the thesis work, the experiments are carried to prove the implementation of PID and reflex based controller using the MLMP-CPG. The reflexes with the CPG generate the adaptive behaviour of the exosuit.

Neuro-computationale Modellierung der Basal Ganglien für Stopp-Signal Aufgaben

Iliana Koulouri

Tue, 12. 3. 2019, Room 132

Baladron, Nambu und Hamker haben 2017 ein biologisch motiviertes neuro-computationales Basalganglienmodell für die Aktionsauswahl während einer Erkundungsphase nach Änderung von Umweltkonditionen durch die GPe-STN Schleife entwickelt. Die Basalganglien beeinflussen ebenso die Unterdrückung von Handlungen. Es wird angenommen, dass ein Rennen zwischen Go-Signalen und Stopp-Signalen einem Rennen zwischen den neuronalen Bahnen der BG entspricht. Dazu postulierten Schmidt et al. (2013) und Mallet et al. (2016) ein zweistufiges Abbruchmodell, welches einen Pause- und einen Abbruchmechanismus beinhaltet. Der Abbruch einer Handlung werde durch einen neu entdeckten Zelltyp, den arkypallidalen globus pallidus Zellen, realisiert. Über ihre Vernetzung sind bislang nicht alle Einzelheiten bekannt. Die vorliegende Arbeit befasst sich mit der Erweiterung des BG Modells von Baladron et al. (2017) um die Implementierung dieses postulierten zweistufigen Abbruchmodells, der Replizierung der Ergebnisse von Mallet et al. (2016) und der Untersuchung des Einflusses der Stärke der Inputsignale, sowie ihre Verzögerung auf das Modell. Es zeigt sich, dass das erweiterte Modell die Ergebnisse von Mallet al al. (2016) erfolgreich replizieren kann. Dabei scheint auch der Kortex als Quelle der Erregung der arkypallidalen globus pallidus Zellen als plausibel. Das Modell weist einen geringen Einfluss gegenüber Änderungen der Stoppraten und der zeitlichen Verzögerung zwischen Go- und Stopp-signalen auf. Allerdings erweist es sich als empfindlich gegenüber Änderungen der Goraten.

Decorrelation and sparsification by homeostatic inhibitory plasticity in cooperation with voltage-based triplet STDP for receptive field formation

René Larisch

Mon, 4. 3. 2019, Room 202

Several recent models have shown how V1 simple-cell receptive fields may emerge from spike-timing dependent plasticity (STDP) mechanisms. These approaches mostly depend on normative learning paradigms, such as maximizing information transmission or optimizing sparseness, or on simplified rate-based learning dynamics. These assumptions make it unclear how inhibition influences neuronal properties (for example orientation selectivity and contrast invariance tuning curves) and network effects (for example input encoding). We here propose a model of V1 based on a phenomenological excitatory and an inhibitory plasticity rule. Our simulation results showing the interplay of excitatory and inhibitory plasticity on the connectivity structure and the influence on input encoding. Further, we demonstrate how the right amount of excitatory and inhibitory input leads to the occurrence of contrast invariance tuning curves.

A network model of the function and dynamics of hippocampal place-cell sequences in goal-directed behavior

Lorenz Gönner

Fri, 15. 2. 2019, Room 336

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, I propose a spiking network model of spatial learning and sequence generation as interdependent processes. Simulations show that this model explains the generation of never-experienced sequence trajectories in familiar environments and highlights their utility in flexible route planning. In addition, I report the results of a detailed comparison between simulated spike trains and experimental data, at the level of network dynamics. These results demonstrate how sequential spatial representations are shaped by the interaction between local oscillatory dynamics and external inputs.

Computing with hippocampal sequences

Prof. Dr. Christian Leibold

Fri, 15. 2. 2019, Room 336

Hippocampal place cells are activated in sequence on multiple time scales during active behavior as well as resting states. Reports of prewired hippocampal place cell sequences that were decoded as future behaviors are hard to bring into register with the suspected roles of the hippocampus in the formation of autobiographic episodic memories and spatial learning. Here, I propose a computational model, that shows how a set of predefined internal sequences that are linked to only a small number of salient landmarks in a large random maze can be used to construct a spatial map of a previously unknown maze.

Adaption of a neuro-computational model of space perception to new physiological data and evaluation with experimental data from human and monkeys

Nikolai Stocks

Wed, 13. 2. 2019, Room 132

A. Zietsche and F.H. Hamker developed a computational model for the simulation visual perception and perisaccadic mislocalisation [A. Ziesche et al. 2011]. .A paper by Joiner et al. published in 2013 demonstrates that monkeys do perform different than humans in Saccadic Supression of Displacement (SSD) trials under the stimulus blanking condition which the model does not account for. Furthermore does data by Xu et al. 2012 show that the neural representation of current eye position updates later than the original model accounted for. The goal of this thesis is to find adjustments to the parameters of the original model and to allow it to accurately simulate SSD experiments for both monkeys and humans using Xu's revised timeframe.

Development of Collision Prevention Module using Reinforcement Learning

Arshad Pasha

Wed, 6. 2. 2019, Room 132

The tremendous advancements in the field of application of Artificial Intelligence in Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AVs) have still not been able to curb the accident rate. Many automotive manufacturers aim to achieve the 'Zero Fatalities' goal with advanced collision prevention modules by the next decade. This thesis work mainly focuses on the implementation of ADAS functionalities such as Autonomous Emergency Braking (AEB), Autonomous Emergency Steering (AES), and Adaptive Cruise Control (ACC) using Reinforcement Learning (RL). RL has attracted researchers' attention in recent years due to its immense potential. Q-Learning is one of the most exploited RL algorithms. It has not only been employed to solve a simple problem as grid world problem but also to solve complex problems as playing advanced levels video games such as Doom, Breakout and various other Atari games. This thesis work also uses the Q-learning algorithm. This thesis was carried out at the Research and Development unit of ZF known as Zukunft Mobility GmbH, Kösching, Germany. The principal objective of this thesis is to use Tabular Q-learning approach to implement complex ADAS functionalities as mentioned earlier and analyze the RL agent's behavior in addition to integrating these developed algorithms within the organization's proprietary simulation environment (SimpleSpec). Further, the continuous State-Action space has been discretized to adapt to the tabular Q learning approach which avoids the 'Curse Of Dimensionality' and 'Matrix Explosion' problems. In the penultimate part, a qualitative and quantitative evaluation of the performance has been carried out. The results obtained after a full-scale model testing for low, medium and high-speed scenarios have been recorded. In addition, random scenarios were generated to test the scope and capabilities of the trained RL agent. Further research has been carried out in the area of the impact of reward function structure on the RL agent which includes the sparse and non-sparse reward structure design and their influence on the reward convergence and learning process. Further, this segment also includes the analysis between the explicitly distributed non-sparse reward design and the non-sparse inline reward design. The final part summarizes the tasks covered, the goal achieved and draws a path to the future work.

Road scene semantic segmentation using residual factorized convnet and surround view fisheye cameras

Saiffuddin Syed

Wed, 30. 1. 2019, Room 367a

Automotive industry is continuously evolving, especially in the self-driving domain which creates a demand for new concepts to be developed, implemented and tested. At present the only sensor capable of sensing the immediate surrounding of the vehicle is a camera.This thesis addresses the 360 degrees road scene semantic segmentation problem for fisheye cameras. Present vehicles are equipped with distinct types of cameras used for various practical real-time applications, the most common camera model being the wide-angle fisheye cameras which are considered for this thesis. Usage of this camera brings two major challenges: firstly, CNN-based semantic segmentation task requires a huge amount of pixel-level annotated data. So far there is no open-source annotated dataset available for wide-angle images. Secondly, a fisheye camera introduces severe distortions and negates the positional invariance offered by a conventional pinhole camera model. To overcome this, training the images on transformed images that are augmented using a fisheye filter is proposed. An approach to integrate blocks which improve the representational power of existing architectures by explicitly modelling interdependencies between channels of convolutional features, has been tested. The experiments carried out prove the effectiveness of these blocks when augmented data is used. Much of the work presented in the thesis was devoted to a rigorous comparison of the architectures.The evaluation of the thesis is done on two different kind of datasets, a real world dataset and a synthetic dataset. The primary metric used for evaluation was the Intersection-over-Union (IoU). The results at the end of the thesis showed that a large amount of existing annotated data taken from pinhole cameras can be reused through augmentation and relatively small amount of annotated from fisheye cameras is required to account for domain shift. Further, the new architectures presented in this thesis show promising results when applied to augmented data.

A network model of the function and dynamics of hippocampal place-cell sequences in goal-directed behavior

Lorenz Gönner

Mon, 21. 1. 2019, Room 219

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, I propose a spiking network model of spatial learning and sequence generation as interdependent processes. Simulations show that this model explains the generation of never-experienced sequence trajectories in familiar environments and highlights their utility in flexible route planning. In addition, I report the results of a detailed comparison between simulated spike trains and experimental data, at the level of network dynamics. These results demonstrate how sequential spatial representations are shaped by the interaction between local oscillatory dynamics and external inputs.

Categorizing facial emotion expressions with attention-driven convolutional neural networks

Valentin Forch

Mon, 21. 1. 2019, Room 219

The development of so-called deep machine learning techniques has brought new possibilities for the automatic processing of emotion-related information which can have great benefits for human-computer interaction. Vice versa machine learning can profit from concepts known from human information processing (e.g., visual attention). Being located in the spectrum of human and artificial intelligence, the aim of the present thesis was twofold: (a) to employ a classification algorithm for facial expressions of emotions in the form of a deep neural network incorporating a spatial attention mechanism on image data of facial emotion expressions and (b) to compare the output of the algorithm with results from human facial emotion recognition experiments. The results of this thesis show that such an algorithm can achieve state-of-the-art performance in a facial emotion recognition task. With regard to its visual search strategy some similarities with human saccading behavior emerged when the model's perceptive capabilities were restricted. However, there was only limited evidence for emotion-specific search strategies as can be found in humans.

3D reconstruction with consumer depth cameras

Manh Ha Hoang

Wed, 9. 1. 2019, Room 132

In this thesis, we develop an RGB-D camera-based system that is able to generate a 3D model of a single household object using a consumer depth (RGB-D) camera. The system then grabs textures of the object from a high-resolution DSLR camera and applies them to the reconstructed 3D model. Our approach specially addresses on generating a highly accurate 3D shape and recovering high-quality appearance of the object within a short time interval. The high-quality 3D texture object models can be used for the products of online shopping, augmented reality, and further research of 3D Machine Learning.

Hierarchical representations of actions in multiple basal ganglia loops

Javier Baladron

Wed, 5. 12. 2018, Room 132

I will introduce here three novel concepts, tested and evaluated by means of a neuro-computational model that brings together ideas regarding the hierarchical organization of the basal ganglia and particularly assigns a prominent role to plasticity. I will show how this model reproduces the results of two cognitive tasks used to measure the development of habitual behavior and introduce a model prediction.

Investigating reservoir-based reinforcement learning for robotic control

Oleksandr Nikulkov

Wed, 28. 11. 2018, Room 132

Reservoir Computing is a relatively novel approach for training recurrent neural networks. It is based on generating a random recurrent reservoir as a part of the network and training only the readout of the reservoir. This separation makes the setup easy to be implemented and offers different directions for further research to be done. Existing methods for learning cognitive tasks often require continuous reward signals, which are not always available in cognitive tasks. However, this disadvantage can be avoided by using supralinear amplification on the trace of node-perturbation weight updates to suppress the relaxation-effect, as proposed by (Miconi, 2017). In this presentation, I will show how such a network can be applied to a robotic control task and investigate the role of the different parameters.

Model Uncertainty estimation for a semantic segmentation network with a real time network deployment analysis on Nvidia Drive PX2 for Autonomous Vehicles

Abhishek Vivekanandan

Mon, 19. 11. 2018, Room 132

Autonomous vehicles require a high degree of perception capabilities in order to perceive the environment and predict objects therein at a high precision in real time. For such cases we use semantic segmentation networks. A major challenge in using semantic segmentation is determining how confident the network is in its prediction or in other words how trustworthy classification outcomes are. Integrating uncertainty estimates with semantic segmentation help us to understand the confidence measure with which a network predicts its output. Bayesian approaches along with dropouts provide us the necessary tool in deep learning to extract the uncertainty involved in the prediction from a model. In Bayesian Neural Networks, we place a distribution over the weights, giving us a probabilistic interpretation about the classification. For such networks, multiple Monte Carlo sampling is needed to generate a reliable posterior distribution from which we can infer uncertainty statistics. The serial nature of this sampling approach restricts its use in the real time environment. In this work through in-depth analysis we show the best possible places in a neural network to deploy dropouts along with the number of MC sampling which needs to be done such that we can maximize the quantifications to estimate uncertainty. We also exploit parallel capabilities of GPU to realize certain neural operations such as convolution and dropouts directly on an embedded hardware with minimal abstraction. As a result we propose the necessary alternative changes to the kernel functions needed to implement parallel Monte Carlo dropout sampling to estimate uncertainty in real-time. Finally, we provide a brief comparison in terms of benchmarking about the kernel implementations on a CPU (Intel Xeon processor) and a GPU (DrivePX2 and Nvidia Geforce 1080Ti).

Disentangling representations of grouped observations in adversarial autoencoders

Felix Pfeiffer

Wed, 14. 11. 2018, Room 131

Being able to classify the shown emotion or facial action from mere pictures of faces is a challenging task in machine learning, since simple classification requires at least reliably labeled data, which is hard to get in sufficient quantity. Unsupervised learning methods can at least in part avoid the problem of dependency from such data, by finding representations that are meaningful. In my thesis I present an algorithm that teaches an Adversarial Autoencoder how to find representations of data. With clever administration of the training process it is possible to strip information from the representation that would not be beneficial for specific tasks like classification. This process is called disentangling and the administrative strategy is to find groups of data. I will show the results of some experiments that verify that the algorithm does what it promises and elaborate on where its weaknesses may be, by training an Adversarial Autoencoder on a colorful MNIST dataset and let it produce disentangled representations that separate style from content.

Interpreting deep neural network-based models for automotive diagnostics

Ria Armitha

Wed, 7. 11. 2018, Room 131

With the breakthrough of Artificial intelligence over the last few decades and extensive improvements in Deep Learning methodologies, the field of Deep Learning has gone through major changes. AI has outdone humans in computing complex tasks like object and image recognition, fault detection in vehicles, speech recognition, medical diagnosis etc. From a bird's-eye view the models are basically algorithms which try to learn concealed patterns and relationships from the data fed into it without any fixed rules or instructions. Although these models' prediction accuracies may be impressive, the system as a whole is a black-box (non-transparent). Hence, explaining the working of a model to the real world poses its own set of challenges. This work deals with interpreting vehicle fault-detection model. Current fault detection approaches rely on model-based or rule-based systems. With an increasing complexity of vehicles and their sub systems, these approaches will reach their limits in detecting fault root causes in highly connected and complex systems. Furthermore, current vehicles produce rich amounts of data valuable for fault detection which cannot be considered by current approaches. Deep Neural Networks (DNN) offer great capabilities to tackle these challenges and automatically train fault detection models using in-vehicle data. However, fault detection models based on DNNs (here, CNNs and LSTMs) are black boxes so it is nearly impossible to back-trace their outputs. Therefore, the aim of this work is to identify, implement and evaluate available approaches to interpret decisions made by DNNs applied in vehicle diagnostics. With that, decisions made by the DNN diagnostics model can be better understood to (i) comprehend the model's outputs and thus increase model performance as well as (ii) enhance their acceptability in vehicle development domain.

Learning the Motor Program of a Central Pattern Generator for Humanoid Robot Drawing

Deepanshu Makkar

Thu, 1. 11. 2018, Room 132

In this research project, we present a framework where a humanoid robot, NAO, acquires the parameter of a motor program in a task of drawing arcs in Cartesian space. A computational model based on Central Pattern Generator is used. For the purpose of drawing a scene, geometrical features such as arcs are extracted from images using Computer Vision algorithms. The algorithm used in the project which considers only important features for the purpose of robot drawing is discussed. These arcs can be described as a feature vector. A discussion is done on how genetic algorithms help us in parameter estimation for the motor representation for selected feature vector. This understanding of parameters is used further to generalize the acquired motor representation on the workspace. In order to have a generalization for achieving a mapping between the feature vector and the motor program, we propose an approximation function using a multilayer perceptron (MLP). Once the network is trained, we present different scenarios to the robot and it draws the sketches. It is worth noting that our proposed model generalizes the motor features for a set of joint configuration, unlike the traditional way of robots drawing by connecting intermediate points using inverse kinematics.

Cortical routines - from experimental data to neuromorphic brain-like computation

Prof. Dr. Heiko Neumann (Ulm University, Inst. of Neural Information Processing)

Tue, 30. 10. 2018, Room 1/336

A fundamental task of sensory processing is to group feature items that form a perceptual unit, e.g., shapes or objects, and to segregate them from other objects and the background. In the talk a conceptual framework is provided, which explains how perceptual grouping at early as well as higher-level cognitive stages may be implemented in cortex. Different grouping mechanisms are implemented which are attuned to basic features and feature combinations and evaluated along the forward sweep of stimulus processing. More complex combinations of items require integration of contextual information along horizontal and feedback connections to bind neurons in distributed representations via top-down response enhancement. The modulatory influence generated by such flexible dynamic grouping and prediction mechanisms is time-consuming and is primarily sequentially organized. The coordinated action of feedforward, feedback, and lateral processing motivates the view that sensory information, such as visual and auditory features, is efficiently combined and evaluated within a multiscale cognitive blackboard architecture. This architecture provides a framework to explain form and motion detection and integration, higher-order processing of articulated motion, as well as scene segmentation and figure-ground segregation of spatio-temporal inputs which are labelled by enhanced neuronal responses. In addition to the activation dynamics in the model framework, steps are demonstrated how unsupervised learning mechanisms can be incorporated to automatically build early- and mid-level visual representations. Finally, it is demonstrated that the canonical circuit architecture can be mapped onto neuromorphic chip technology facilitating low-energy non-von Neumann computation.

Neural Reflexive Controller for Humanoid Robots Walking

Rishabh Khari

Thu, 25. 10. 2018, Room 131

For nearly three decades, a great amount of research emphasis has been given in the study of robotic locomotion, where researchers, in particular, have focused on solving the problem of locomotion control for multi-legged humanoid robots. Especially, the task of imitating human walking has since been the most challenging one, as bi-pedal humanoid robots implicitly experience instability and tend to topple itself over. However, recently new machine learning algorithms have been approached to replicate the sturdy, dexterous and energy-efficient human walking. Interestingly many researchers have also proposed that the locomotion principles, although run on a centralized mechanism (central pattern generator) in conjunction with sensory feedback, they can also independently run on a purely localized sensory-feedback mechanism. Therefore, this thesis aims at designing and evaluating two simple reflex-based neural controllers, where the first controller generates a locomotion pattern for the humanoid robot by combining the sensory feedback pathways of the ground and joint sensors to the motor neuron outputs of the leg joints. The second controller makes use of the Hebb's learning rule by first deriving locomotion patterns from the MLMP-CPG controller while observing the sensory feedback simultaneously and finally generating motor-neuron outputs associatively. In the end, this thesis also proposes a fast switching principle where the output to motorneurons after a certain interval is swiftly transferred from the MLMP-CPG to the associative reflex controller. This is implemented to observe adaptive behavior present for centralized locomotor systems.

Improving autoregressive deep generative models for natural speech synthesis

Ferin Thunduparambil Philipose

Wed, 24. 10. 2018, Room 132

Speech Synthesis or Text To Speech (TTS) synthesis is a domain that has been of research interest for several decades. A workable TTS system would essentially generate speech from textual input. The quality of this synthesized speech would be gauged based on how similar it sounds to the human voice and the ease of understanding it clearly. .A fully end to end neural Text-To-Speech system has been set up and improved upon, with the help of WaveNet and Tacotron deep generative models. The Tacotron network acts as a feature prediction network that outputs the log-mel spectrograms, which are in-turn utilized by WaveNet as the local conditioning features. Audio quality was improved by the logmel local conditioning and the fine-tuning of hyper-parameters such as mini-batch size & learning rate. Computational effort was reduced by compressing the WaveNet network architecture.

Fatigue detection using RNN and transfer learning

Azmi Ara

Wed, 24. 10. 2018, Room 132

Driving car is a insecure activity which requires full attention. Any distraction can lead to dangerous consequences, such as accidents. While driving, many factors are involved, such as: fatigue, drowsiness, distractions. Drowsiness is a state between alert and sleep. For this reason, it is important to detect drowsiness in advance which will help in protecting the people from accidents. The research guides us to understand an implicit and efficient approach to detect the different levels of drowsiness. Every driver has different driving patterns. The developed system should be able to adopt to the changes of driver?s behavior. The aim of this thesis is to contribute to the study of detecting drivers drowsiness levels while driving through different approaches which integrates of two sensory data to improve detection performance.

Car localization in known environments

Prafull Mohite

Tue, 2. 10. 2018, Room 131

Localization in a broader sense is very wide topic and at present basic localization takes place with the help of GPS sensor but lacks accuracy which is important for Autonomous driving. To overcome this problem, there are different environmental sensors used (typically, Sonar, Lidar, Camera). Lidar sensor being very accurate in case of depth perception is the used. In this thesis, Simultaneous Localization And Mapping (SLAM) approach is selected. SLAM, as name suggested Localization and mapping is chicken egg problem and to solve it, we are creating map of an environment before performing localization. For mapping, Gmapping and for localization within map, Adaptive Monte Carlo Localization (AMCL) is selected. AMCL is basically a particle filter. After giving a map of an environment, the algorithm estimates the position and orientation of a car as it moves and senses the environment.

Image anonymization using GANs

Thangapavithraa Balaji

Mon, 24. 9. 2018, Room 131

Millions of images are being collected every day for applications to enable scene understanding, decision making, resource allocation and policing to ease the human life. Most of these applications doesn't require the identity of the people in the images.There is an increasing concern in these systems invading the privacy of the users and the public. On one side, the camera/robots can assist a lot in everyday life, but on the other side, the privacy of the user or the public should not be compromised. In this master thesis, a novel approach was implemented to anoymize faces in the datasets which enable privacy protection of the individuals in the datasets. The Generative Adversarial Network(GAN) approach was extended and the loss function was formulated in a combined fashion. The performance of conventional image anonymization techniques like blurring, cropping, pixelating were compared against GAN generated images using autonomous driving applications like object detection and semantic segmentation.

Training approaches onsemantic segementation using transfer learning, dataset quality assessment and intelligent data augmentation

Mohamed Riyazudeen Puliadi Baghdad

Mon, 24. 9. 2018, Room 131

Data Sparsity is one of the key problems that automotive industries face today. One way to overcome this is to use synthetic data that are generated from graphics engines or virtual world generator, that can be leveraged to train neural networks and accomplish tasks such as autonomous driving. The features learned from synthetic data yield better performance with a suitable training approach and some real data. The number of images in the synthetic dataset, and its similarity to real world dataset play a major role in transferring the learned features effectively across domains. This similarity in the distribution of these datasets was achieved through different approaches, the most effective one being Joint Adaptation Network Approach. Also, data augmentation in a smart way could boost the performance achieved. Intelligent data augmentation was achieved using conditional Generative Adversarial Networks and Color Augmentation technique. With the findings of this research work, a possible solution for tackling data sparsity problem was achieved.

Investigating Model-based Reinforcement Learning Algorithms for Continuous Robotic Control

Frank Witscher

Wed, 19. 9. 2018, Room 368

Obwohl model-free, deep Reinforcement Learning eine immer größer werdende Bandbreite an Aufgaben erfüllen kann, leiden die jeweiligen Algorithmen an einer großen Ineffizienz bezüglich der dafür erforderlichen Datenmenge. Model-based Reinforcement Learning, welches ein Dynamics Model der Umwelt erlernt, verspricht hierbei Abhilfe. Jüngste Forschungen kombinieren model-free Algorithmen mit model-based Ansätzen, um die Stärken beider Reinforcement Learning-Zweige auszunutzen. In meiner Verteidigung gebe ich eine Einleitung in model-based Reinforcement Learning und einen Überblick über die mögliche Nutzung von Dynamics Models, wie sie in neusten Publikationen zu finden ist. Wir konzentrieren uns dabei auf Umgebungen mit kontinuierlichen Action Spaces, wie sie in der Robotik anzutreffen sind. Temporal Difference Model ist ein solcher Hybrid aus model-free Learning mit model-based Control. Dieser wird im Detail vorgestellt und ausgewertet.

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