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Forschungsseminar

Forschungsseminar

Das Forschungsseminar ist eine Veranstaltung, die sich an interessierte Studenten des Hauptstudiums richtet (bzw. Master oder höheres Semester Bachelor). Andere Interessenten sind jedoch jederzeit herzlich willkommen! Die vortragenden Studenten und Mitarbeiter der Professur KI stellen aktuelle forschungsorientierte Themen vor. Vorträge werden in der Regel in Englisch gehalten. Das Seminar findet unregelmäßig im Raum 336 statt. Den genauen Termin einzelner Veranstaltungen entnehmen Sie bitte den Ankündigungen auf dieser Seite.

Informationen für Diplom- und Masterstudenten

Die im Studium enthaltenen Seminarvorträge (das "Hauptseminar" im Studiengang Diplom-IF/AIF bzw. das "Forschungsseminar" im Master) können ebenso im Rahmen dieser Veranstaltung durchgeführt werden. Beide Lehrveranstaltungen (Diplom-Hauptseminar und Master-Forschungsseminar) haben das Ziel, dass die Teilnehmer selbststängig forschungsrelevantes Wissen erarbeiten und es anschließend im Rahmen eines Vortrages präsentieren. Thematisch behandeln die Seminare das Gebiet der Künstlichen Intelligenz, wobei der Schwerpunkt auf Objekterkennung, Neurocomputing auf Grafikkarten und Multi-Core Rechnern, Reinforcement Lernen, sowie intelligente Agenten in Virtueller Realität liegt. Andere Themenvorschläge sind aber ebenso herzlich willkommen!
Das Seminar wird nach individueller Absprache durchgeführt. Interessierte Studenten können unverbindlich Prof. Hamker kontaktieren, wenn sie wenn sie ein Interesse haben, bei uns eine der beiden Seminarveranstaltungen abzulegen.

Kommende Veranstaltungen

Image anonymization using GANs

Thangapavithraa Balaji

Mon, 24. 9. 2018, 12:00, 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, 11:30, 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.

Vergangene Veranstaltungen

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.

Sensor simulation and Depth map prediction on Automotive Fisheye camera using automotive deep learning

Deepika Gangaiah Prema

Wed, 12. 9. 2018, Room 131

The aim is to create a synthetic 3D environment which enables to obtain a supervised dataset using Unity framework and simulating different sensors like lidar and fisheye camera in the simulation environment. This dataset will be used to develop, test and validate different machine learning algorithms for automotive use cases. The big advantage of the simulation environment is the possibility to generate data from different sensors which are still under development and the final hardware is still not available. Another advantage is that the known ground truth of the simulation environment. This much cheaper than equipping a vehicle with those sensors, record lots of data and manually label the ground truth by humans. The 3D environment shall include urban and highway driving scenarios with balanced object categories like vehicles, pedestrians, trucks, terrain and street or free space to cover all levels for autonomous driving The simulation of a fish eye camera such as next generation lidar will be carried out in the thesis on the same Unity 3D framework, the generated images and point cloud data are used to generate different data sets. The final goal is to use this for training different models and test them on a real environment. Qualitative test are carried out by benchmarking the data sets with the aid of different algorithms. The aim of this thesis is to study the different approaches with which CNNs could be used in the task of depth estimation from a single fisheye camera image (180 degree FoV) for Autonomous Driving.

Humanoid robot learns walking by human demonstration

Juncheng Hu

Tue, 14. 8. 2018, Room 131

In this thesis, a method designed for making the humanoid robot walking is developed by using the Q learning based on MLMP-CPG and wrist sensors. Machine learning has demonstrated a promising feature in many fields including robotics. However, the supervised learning algorithms are more often applied. However, supervised learning like neural networks always need a massive amount of data to train, which is sometimes not permitted in the real situation. Although not much data is required in reinforcement learning, it needs many attempts in its environment thus concluding a strategy. For a humanoid robot, it is not allowed to have too many wrong attempts because a fall may lead to the injury of joints. In this thesis, a method that the robot learns walking with the help of a human can avoid accidental fallings is proposed.

Digital Twin Based Robot Control via IoT Cloud

Tauseef Al-Noor

Tue, 14. 8. 2018, Room 131

Digital Twin (DT) technology is the recent key technology for Industry 4.0 based monitoring and controlling industrial manufacturing and production. There are a lot of researches and development happening on DT based robot control. Monitoring and controlling the robot from a remote location is a complex process. In this research work, I have developed a prototype for controlling a robot using DT and cloud computing. Different technologies and techniques related to Digital Twin have been researched and analyzed to prepare an optimal solution based on this prototype. In this work, the latency of different types of machine to machine (M2M) communication protocols is observed. Different type of network protocols such as AMQP, MQTT, and HTTP has a lot of latency variation in the end to end data transfer communication. Furthermore, different external factors impact on persistent communication. For example, the cloud computing service as like as Azure?s data processing and throughput is not constant-time. A robot controlling mechanism expects a minimum constant time response for the quality of service. In this research, the main focus was to minimize communication latencies for a remote robot controller in a cloud-based communication. Finally, an average quality of service in the range of 2-5 seconds for persistent robot communication has been achieved based on different setup.

Vision-based Mobile Robotics Obstacle Avoidance with Deep Reinforcement Learning

Zishan Ahmed

Wed, 8. 8. 2018, Room 131

Obstacle avoidance is a fundamental and challenging problem for autonomous navigation of mobile robots. In this thesis, the problem of obstacle avoidance in simple 3D environments where the robot has to rely solely on a single monocular camera is considered. Inspired by the recent advantages of deep reinforcement learning (DRL) in Atari games and understanding highly complex situations in Go, the obstacle avoidance problem is tackled in this thesis as a data-driven end-to-end deep learning approach. An approach which takes raw images as input, and generates control commands as output is presented. The differences between discrete and continuous control commands are compared. Furthermore, a method to predict the depth images from monocular RGB images using conditional Generative Adversarial Networks (cGAN) is presented and the increase in learning performance by additionally fusing predicted depth images with monocular images is demonstrated.

Deep Convolutional Generative Adversarial Networks (DCGAN)

Indira Tekkali

Tue, 24. 7. 2018, Room 132

Generative Adversarial Networks (GAN) have made great progress in the recent years. Most of the established recognition methods are supervised, which have strong dependence on image labels. However obtaining large number of image labels is expensive and time consuming. In this project, we investigate the unsupervised representation learning method that is DCGAN. We base our work on previous paper by Radford and al., and aim to replicate their results. When training our model on different datasets such as MNIST, CIFAR-10 and Vehicle dataset, we are able to replicate some results for e.g. smooth transmission.

Using Transfer Learning for Improving Navigation Capabilities of Common Cleaning Robot

Hardik Rathod

Tue, 10. 7. 2018, Room 131

A lot of robotic vacuum cleaners fail during the cleaning task because they get stuck under furniture or within cords or some other objects on the floor. Once such situation occurs, the robot is hardly able to free itself. One possible cause of this behavior is insufficient information of the environment, the robot enters. In unstructured environments, recognition of objects has been proven to be highly challenging. By executing an analysis of the environment before the cleaning operation starts, the robot will be aware of the objects around it, especially those that might harmful in the navigation. Methods from machine learning have been investigated and tested as they give impressive results in object detection tasks. Taking adequate actions according to objects in the environment helps to overcome or reduce the possibilities to getting stuck the robot under the objects, and eventually it reduces the effort of the customers. The insight from this analysis has been incorporated within the locomotion behavior of a dummy robot.

Vergence control on humanoid robots

Torsten Follak

Mon, 9. 7. 2018, Room 131

For the orientation in the 3D-space, a good depth is needed. This estimation is reached through effective stereoscopic vision. There the disparity between both eyes images is used to derive the 3D-structure. Therefore, it is important that both eyes are fixating at the same point. This fixation is managed by vergence control. To implement and use vergence control in robotics, different approaches exits. In this talk three of them are shown. A short overview of the two first is given, while the third one is presented in detail.

Docker for machine learning

Alexander J. Knipping and Sebastian Biermann

Tue, 3. 7. 2018, Room 131

Handling software dependencies for research and or production environments often comes with a certain amount of complexity. Libraries like TensorFlow or PyTorch don't always behave in the same way across several major version releases, especially in combination with various other third-party libraries, different Python versions and CUDA toolkits. Several solutions such as anaconda, virtualenv or pyenv have emerged from the Python community, but managing those in regards to reproducibility and portability often feels clumsy and leads to unexpected errors, especially for system administrators. In this presentation we will evaluate if Docker containers can be a more efficient way to encapsulate project code with its dependencies, to build once, ship anywhere. For demonstration we have used Docker to train a machine learning model able to recognize 194 birds by their calls, through a variation of an existing, VGG based, model trained on Google's Audioset and using their feature extractor for our own classes. Training was then performed on over 80 000 audio files of ten to twenty seconds length on nucleus. We will demonstrate how we have used Docker in our workflow from developing the model, training it on the nucleus node to deploying the model into a productive environment for users to query it. The aim of our project is to provide both users and system administrators an overview of how Docker works, what its benefits and costs are and if it's a viable option to use in typical machine learning workflows and environments.

Humanoid robots learn to recover perturbation during swing motion in frontal plane: mapping pushing force readings into appropriate behaviors

Ibrahim Amer

Tue, 19. 6. 2018, Room 131

This thesis presents a learning method to tune recovery actions for humanoid robot during swinging movement based on central pattern generator. A continuous state space of robot is learned through self-organized map. A disturbance detection technique is proposed based on robot states and sub-states. Predefined recovery actions space are used in this thesis and they are composed of non-rhythmic patterns. A hill climb algorithm and a neural network have been used to tune the non-rhythmic patterns parameters to obtain the optimum values. A humanoid robot NAO was able to recover from disturbance with an adaptive reaction based on disturbance amplitude. All experiments were done on Webots simulation.

Humanoid robot grasping in 3D space by learning an inverse model of a central pattern generator

Yuxiang Pan

Tue, 19. 6. 2018, Room 131

Grasping is one of the most important functions of humanoid robots. However, an inverse kinematics model for the robot arm is required to reach an object in the workspace. This model can be mathematically described using the exact robot parameters, or it can be learned without a prior knowledge about these parameters. The later has an advantage as the learning algorithm can be generalized to other robots. In this thesis, we propose a method to learn the inverse kinematics model of NAO humanoid robot using a multilayer perceptron (MLP) neural network. Robot actions are generated through the multi-layered multi-pattern central pattern generator (CPG) model. The camera captures the information of the object provided by the ArUco markers, the MLP model provides the desired arm configurations to reach the object, and then the CPG parameters are calculated to move the arm from its current position into the goal position. The proposed model have been tested in simulation, and on the real robot where a soft sensory robotic gripper was used to interact with a human subject (tactile servoing). Grasping was done using both the learned inverse model and the sensory feedback.

Scene Understanding on a Humanoid Robotic Platform Using Recurrent Neural Networks

Saransh Vora

Wed, 13. 6. 2018, Room 131

Since near perfect levels of performance have been reached for object recognition using convolutional neural networks. The ability to describe the content and organization of a complex visual of the scene is called scene understanding. In this thesis the deterministic attention model has been used with back propagation with two different pre-trained encoder CNN models along with a RNN as a decoder to generate captions. The trained attention model is then used for a humanoid robot to describe the scene. This would represent first step towards robotic scene understanding. The robot can not only associate words with images but it can also point at the locations of features which are attended to and locate them in space.

Transferring deep Reinforcement Learning policies from simulations to real-world trajectory planning

Vinayakumar Murganoor

Tue, 5. 6. 2018, Room 131

Machine learning is really progressed a lot in recent days but most of the applications and demonstrations are done in simulated environments, especially with continuous control tasks. When it comes to continues control tasks, the reinforcement learning algorithms are proven to produce the good policies. In this project, the problem of trajectory planning is solved using reinforcement learning algorithms, where the simulated trained agent in the real-world moves the RC car from any given point A to point B with no training in real world itself. Also identified the minimum parameters that influence the agent behavior in the real world and listing out the problems and solutions found during the transfer of the policy from simulation to the real world.

Investigating dynamics of Generative Adversarial Networks (GANs)

Vivek Bakul Maru

Tue, 29. 5. 2018, Room 131

Generative Adversarial Networks (GANs) are very recent and promising approach in generative models. GANs are the approaches to solve problems by unsupervised learning using deep neural networks. GANs work on an adversarial principle, where two different neural networks are fighting with each other to get better. This research project aims to understand the underlying mechanism of GANs. GANs certainly have proved to have an edge over all the existing generative models like Variational autoencoders and Autoregressive models but they are known to suffer instability while training. Implementation research in this project focuses on investigating the issues regarding training of GANs and the convergence properties of the model. Apart from vanilla GAN, this project also focuses on the extension of regular GAN using convolutional neural networks, called Deep Convolutional GAN and one of the very recently proposed approaches called, Wasserstein GAN. Analysis of the travel of the loss functions insights into the convergence properties. Training of these models on multiple datasets allowed to compare and observe the learning of both the networks in GAN.

Design and Fabrication of Complex Sensory Structure of Curving and Pressure Sensors for Soft Robotic Hand

Vishal Ghadiya

Wed, 23. 5. 2018, Room 131

This Research project represents the prototype design of the complex sensory structure for a soft hand. This can be easily adapted to the soft material like silicon. A superposition of four piezoresistive pressure sensors and one curving sensor was arranged on the inner face of each finger. This research focuses on the design of flexible pressure and curving Sensors, in particular to the response of force sensitive resistor based pressure sensor. Thanks to the multi-layered design of Sensors, the structure was able to measure the curve of the finger and amount of tactile pressure applied by the object grasped to the hand. Sixteen pressures sensor and four curving sensors with Velostat as piezoresistive layer were designed with a diversity of electrode material i.e. conductive thread, with and without using of conductive fabric. The multilayer structure of pressure and the curving sensor can be placed on the inner face of the soft hand and easily evaluate the properties of the object such as size and stiffness of the object.

Erforschung der Rolle von Aufmerksamkeit in rekurrenten Modellen von deep reinforcement learning

Danny Hofmann

Wed, 9. 5. 2018, Room 1/131

Viele Probleme erfordern das erlernen einer Strategie direkt auf den Ausgabedaten der Umgebung, wie Pixelbilder einer Simulation oder auch Kamerabilder eines Roboterarmes. Es wird die Theorie von RAM und der Glimps-Aufmerksamkeit besprochen (Mnih, Heess und Graves 2014) dazu wurde der von Jung 2017 implementierte Algorithmus Asynchronous Attentional Advantage Actor-Critic (A4C), mit kontinuierlichen Aktionsräumen kompatibel gemacht. Die so neu gewonnene Eingabe und Ausgabe Verarbeitung wurde mithilfe eines einfach simulierten Roboterarmes getestet. Es wird Besprochen wie verschiedene Konfigurationen der Simulation zeigen, welche Informationen für den Aufmerksamkeitsmechanismus von Bedeutung sind und wie es dem Agenten gelingt einen Lösungsweg zu finden. Die Ergebnisse lassen einen Vergleich mit anderen Ansätzen des Deep Reinforcement Learnings zu. Unter anderem werden die entstandenen Ergebnisse mit den von Lötzsch 2017 entwickelten DDPG-Varianten verglichen. Es konnte mit A4C keine schnellere Lösung als mit den DDPG-Varianten gefunden werden, jedoch ist es mit A4C möglich nach dem Ende-zu-Ende-Prinzip zu lernen. Demnach lernte A4C direkt auf Bilddaten, wohingegen in den Implementierungen von Lötzsch 2017 eine Abstrahierung der Umwelt benötigt wurde.

A Framework for Adaptive Sensory-motor Coordination of a Humanoid-Robot

Karim Ahmed

Wed, 2. 5. 2018, Room 1/131

This project was done over the research area of sensory-motor coordination on humanoid robots and the ability to generate the coordination for new motor skills. The goal is to modulate the sensory motor connections that generate independently the same motor coordination demonstrated using the Central Pattern Generator (CPG). We propose two neural networks, one network for extracting the coordination features of a robot limb moving by the CPG. And the other network for creating a similar coordinated movement on the other limb moving by simple sensory-motor connections (without the CPG). Thanks to the proposed model, different coordination behaviors were presented at the sensory-motor level. A coordinated rhythmic movement generated by the CPG in the first stage can be demonstrated only by a simple sensory motor network (a reflexive controller) in the next stage where no CPG network is involved.

Knowledge Extraction from Heterogeneous Measurement Data for Datasets Retrieval

Gharbi Ala Eddine

Mon, 30. 4. 2018, Room 1/367

Due to the exponential increase of data amount produced on daily basis, innovative solutions are developed to tackle the problem of properly managing and organizing it with crisp and recent technologies such as Artificial Intelligence (AI) frameworks and Big Data Management(BDM) tools. The need for such solutions rises from the fact that our everyday interconnections soars towards pure digitalization. Therefore, leading Information Technology companies strive to come up with ideas to handle this bulky amount of data in the most efficient and concise way. The challenge faced with this huge data amount is not only to properly organize it but rather to make use of it. That is deriving knowledge from unstructured data is as important as structuring it in an effective way. Throughout this thesis, knowledge derivation techniques are applied on the available data in IAV GmbH. Data can be described as data files used to test the implemented software components and hence the importance of its proper organization. This Master thesis investigates and develops a prototypical solution for the organization of data sets as well a concept implementation for additional information extraction from data files. Different problems and solutions related to Knowledge Discovery (KD) and data organization are presented and discussed in details. Furthermore, an overview of the frameworks and algorithms used Data Minig (DM) is given as well.

Gait Transition between Simple and Complex Locomotion in Humanoid Robots

Sidhdharthkumar Vaghani

Mon, 23. 4. 2018, Room 1/367

This project presents the gait transition between the rhythmic and the non-rhythmic behavior during walking of a humanoid robot Nao. In biological studies, two kinds of locomotion behaviors were observed during cat walking on a flat terrain and walking on a ladder (simple and complex locomotion). In this work, both locomotion behaviors were produced on the robot using the multi-layers multi-patterns central pattern generator model (Nassour et al.). We generate the rhythmic behavior from the non-rhythmic one based on the frequency of interaction between the robot feet and the ground surface during the complex locomotion. While complex locomotion requires a sequence of descending control signals to drive each robot step, simple locomotion requires only a triggering signal to generate the periodic movement. The overall system behavior fits with the biological finding in cat locomotion (Marlinski et al.).

On the role of cortex-basal ganglia interactions for category learning: A neuro-computational approach

Francesc Villagrasa Escudero

Mon, 16. 4. 2018, Room 1/367a

Both the basal ganglia (BG) and the prefrontal cortex (PFC) are involved in category learning. However, their interaction in category learning remains unclear. A recent theory proposes that the basal ganglia, via the thalamus, slowly teach the PFC to acquire category representations. To further study this theory, we propose here a novel neuro-computational model of category learning which performs a category learning task (carried out by monkeys in the past to also study this theory). By reproducing key physiological data of this experiment, our simulations show evidence that further support the hypotheses held by the theory. Interestingly, we show that the fast learner (BG) with a performance of 80% correctly teaches a slow learner (PFC) up to 100% performance. Furthermore, new hypotheses obtained from our model have been confirmed by analyzing previous experimental data.

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