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 MasterstudentenDie 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.
Deep Convolutional Generative Adversarial Networks (DCGAN)
Tue, 24. 7. 2018, 11:30, 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.
Humanoid robot learns walking by human demonstration
Tue, 14. 8. 2018, 12:00, Room TBA
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
Tue, 14. 8. 2018, 11:30, Room TBA
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.
Using Transfer Learning for Improving Navigation Capabilities of Common Cleaning Robot
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
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 robot grasping in 3D space by learning an inverse model of a central pattern generator
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.
Humanoid robots learn to recover perturbation during swing motion in frontal plane: mapping pushing force readings into appropriate behaviors
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.
Scene Understanding on a Humanoid Robotic Platform Using Recurrent Neural Networks
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
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
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
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
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
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.
Verbesserung eines Aufmerksamkeitsmodells mit Deep Learning und Data Augmentation
Thu, 22. 3. 2018, Room 1/309
Das von Frederik Beuth und Fred H. Hamker entwickelte Aufmerksamkeitsmodell wurde unter Nutzung des Coil Datensatzes getestet und Verbesserungen implementiert. Durch Anpassungen des im Aufmerksamkeitsmodell integrierten CNN und Optimierung der Struktur der verwendeten Layer, wurde es möglich, die bisherige Erkennungsrate auf Basis eines realitätsnahen Hintergrundes von 42% auf 60% zu erhöhen. Neben diesen CNN Modifikationen bestand ein weiterer Schwerpunkt der Arbeit in der Vereinfachung der eingesetzten Datenbank und mehrfach wiederholten Test- und Anlernvorgängen unter Nutzung der Methodik des Deep Learning. Der im Coil Datensatz enthaltene Hintergrund der Coil Bilder wurden hierfür testweise entfernt und die Coil Objekte wurden mit unterschiedlichen Hintergründen für den Lernprozess verwendet.
Untersuchung der Auswirkungen von Feedback in einem Modell zur Ziffererkennung
Wed, 14. 3. 2018, Room 1/368
Der überwiegende Teil des Informationsflusses im Gehirn erfolgt über rekurrente Verbindungen. Um die Auswirkungen von Rückwärtsverbindungen in einem künstlichen neuronalen Netzwerk zu untersuchen, wurden einem Modell zur Ziffererkennung diese Verbindungen in einem zwei- und dreischichtigen Modell hinzugefügt. Die dritte Schicht ist dabei kategoriespezifisch. Der unüberwachte Lernalgorithmus wurde zusätzlich durch einen externen Aufmerksamkeitseinfluss ergänzt. Diese Veränderungen werden mit dem ursprünglichen zweischichtigem Feedforward-Modell verglichen. Weiterhin werden in diesem Vortrag die Auswirkungen der Feedbackverbindungen auf ein zwei- und dreischichtiges Modell vorgestellt.
Continuous Deep Q-Learning with Model-based Acceleration
Tue, 20. 2. 2018, Room 1/273
Model-free reinforcement learning has been successfully applied to a range of challenging problems and has recently been extended to handle large neuronal network policies and value functions. However, the sample complexity of model-free algorithms tends to limit their applicability to physical systems. To reduce sample complexity of deep reinforcement learning for continuous control tasks a continuous variant of the Q-learning algorithms was introduced. This talk will be about the algorithm called normalized advantage functions (NAF) and the incorporation of a learned model to accelerate model-free reinforcement learning.
Inhibition and loss of information in unsupervised feature extraction
Wed, 14. 2. 2018, Room 1/273
In this talk inhibition as a means for inducing competition in unsupervised learning will be discussed. The focus will be on the role of inhibition in overcoming the loss of information and the loss of information caused by inhibition itself. A learning mechanism for learning inhibitory weights will be introduced and a hypothesis for explaining the function of VIP dis-inhibitory cells in the brain will be proposed.
Development of a Self-Organizing Model of the Lateral Intraparietal Cortex
Tue, 30. 1. 2018, Room 1/336
Visual perception of objects in space depends on a stable representation of the world. Such a robust world centered depiction would be disrupted with every eye movement. The Lateral Intraparietal Cortex (LIP) in the human brain has been suggested to solve this problem by combining the retino-centric representations of objects from the visual input with proprioceptive information (PC) and corollary discharge signals (CD). Thereby it enables a steady positioning of objects in a head-centered frame of reference. Bergelt and Hamker (2016) had build a model of the LIP that included four-dimensional maps of the LIP. In this master thesis a modification of this model is presented introducing hebbian and anti-hebbian learning to achieve a connection pattern that shows a similar behavior without the necessity of a fixed predefined weighting pattern. Thereby a reduction of dimensions is possible, as well as a higher biological plausibility. The model shows the influence of both signals, PC and CD, on the representation of stimuli over the time of a saccade.
Using Recurrent Neural Networks for Macroeconomic Forecasting
Mon, 29. 1. 2018, Room 1/367a
Despite the widespread application of Recurrent Neural Networks (RNNs) in various fields such as speech recognition, stock market prediction and handwriting recognition, they have so far played virtually no role in the field of macroeconomic forecasting (i.e. in the prediction of variables such as GDP, inflation or unemployment). At the moment, not a single central bank in the world uses RNNs for macroeconomic forecasts. The purpose of the talk is to highlight the potential of RNNs in improving macroeconomic forecasting. First, the history of and current knowledge on RNNs are reviewed. Then, the performance of RNNs in forecasting US GDP figures is described and compared to that of the Survey of Professional Forecasters (SPF).