Research Topics for Students

If students are looking for an interesting BSc, MSc or PhD research project along any of the broad research lines below, they are encouraged to get in touch.

Creating a neuroscience-inspired learning interacting robot

In this exciting project we look at neuroscience-inspired architectures for controlling the behavior of a robot. While traditional robots have often been preprogrammed, our new approach will focus on learning robots which will be based on some neuroscience evidence. These navigation and movement concepts are transferred and further developed on a Nao robot which has some speech and vision capabilities. The NeuroBot will learn to associate actions with words and pointing gestures. We want to restrict the fixed manual programming of the robot and emphasize the adaptive autonomous learning in neural networks in combination with restricted instructions via words and simple pointing. Some of the previous and current robot platforms available are shown at http://www.informatik.uni-hamburg.de/WTM/neurobots. Students who participated in our project "Human Robot Interaction" can also suggest a further topic for their theses.

Goals:

Learning navigation from simple multimodal input
Robotic vision: Object recognition and object manipulation
Implementation of neural network algorithms for speech and pointing instructions for NeuroBot
At later stage: vision and speech capabilities for the NAO

Requirements:

Programming skills: C/C++, Python
At least basic knowledge in neural network algorithms and natural language processing
Willingness to work in robotic environment

The thesis can be written either in German or English. All topics could be tailored to be at a bachelor, master level, or phd student level. If you are interested contact us for discussion:

Contact:

Professor Dr. Stefan Wermter, Dr. Cornelius Weber, Dipl.-Inform. Stefan Heinrich

Internet text mining agents based on hybrid and neural learning techniques

Unrestricted potentially faulty text messages arrive at a certain delivery point (e.g. email address or world wide web address). These text messages are scanned and then distributed to one of several expert agents according to a certain task criterion (e.g. language class, news group or semantic library class). Expert agents can dynamically accept or reject a message. Furthermore, the number of expert agents can vary dynamically over time. If the best possible expert agent is not available the next best choice has to be found in order to supply a best possible instant answer. This dynamic unrestricted message routing task is new and hybrid neural/symbolic techniques have not yet been examined for noisy dynamic internet message routing.

Goals:

Development of hybrid neural and symbolic processing methods
Implementation of an hybrid learning agent for text mining

Requirements:

Programming skills: C/C++, Python, Matlab
At least basic knowledge in neural networks/symbolic processing and mining techniques
Willingness to explore data with algorithmic methods

The thesis can be written either in German or English. All topics could be tailored to be at a bachelor, master level, or phd student level. If you are interested contact us so for discussion:

Contact:

Dipl.-Inf. Doreen Jirak, Professor Dr. Stefan Wermter

Neural and fuzzy state machines for intelligent understanding

Hybrid systems are powerful systems from the point of view of knowledge engineering and cognitive science. Neural processing supports for instance learning, robustness and graded plausible interpretation, while symbolic fuzzy processing has advantages e.g. for easy understanding, simple representation and efficient encoding in smaller domains. One particularly new approach is based on the theory of neural preference classes which can be interpreted as neural network preferences or fuzzy symbolic preferences.

Goals:

Coupling of neural network and fuzzy techniques
Development of neural and fuzzy methods for algorithmic learning
Implementation of neural/fuzzy algorithms based on neural preference classes
In particular: Building a natural semantic system based on recurrent networks and symbolic fuzzy transducer

Requirements:

Programming skills: C/C++, Python, Matlab
At least basic knowledge in hybrid neural systems and mining techniques
Willingness to explore data with algorithmic methods

The thesis can be written either in German or English. All topics could be tailored to be at a bachelor, master level, or phd student level. If you are interested contact us for discussion:

Contact:

Dipl.-Inf. Doreen Jirak, Professor Dr. Stefan Wermter

Building integrated miners using knowledge extraction and neural networks

Documents like medical records, newspapers or documents in the World Wide Web often contain structured data as well as unstructured free text. In the future it will be even more essential to use learning and data mining techniques, for instance from neural networks and statistics, to acquire, process, and adapt knowledge from structured and unstructured data.

Goals:

Design and building of prototype system for data- and text mining and information extraction
Combining several hybrid neural architectures and methods
Development and Implementation of hybrid learning toolbox for mining purposes
Evaluation of developed methods on the same datasets and text material

Requirements:

Programming skills: C/C++, Python, Matlab
Basic knowledge in hybrid neural systems and mining techniques
Understanding of algorithmic methods regarding neural networks
Motivation to work in the area of data analysis and data evaluation with statistics

The thesis can be written either in German or English. All topics could be tailored to be at a bachelor, master level, or phd student level. If you are interested contact us for discussion:

Contact:

Dipl.-Inf. Doreen Jirak, Professor Dr. Stefan Wermter

Robot control for recharging

Intelligent robotics is a wide area covering vision, control and bio-inspired learning. In this thesis, a robot action sequence is developed using reinforcement learning. The humanoid robot Nao shall recharge its batteries without the need of an operator.

Goals:

Setup of a simple environment in the Webots 3D simulator and control of the Nao humanoid robot.
Extraction of visual features (for simplicity, one may use "Naomarks" markers) to localise the robot with respect to the recharging station.
Implementation of a reinforcement learning algorithm [1] to control the robot to pose itself suitably in front of the recharging station.
Hard-coded robot movement to get into the recharging pose.

Requirements:

Programming experience, preferably C/C++, Python, OpenCV. Deeper knowledge will be aquired during the thesis.
Keen interest in artificial intelligence, preferably bio-inspired methods such as neural networks.
Collaboration with a technician for the recharging station (simple metal plates).

[1] Goal-Directed Feature Learning. Weber & Triesch (2009), www.informatik.uni-hamburg.de/~weber/publications/09IJCNN_RLfeatures_0660.pdf

For further questions contact any of the below.

Contact:

Professor Dr. Stefan Wermter, Dr. Cornelius Weber, Nicolas Navarro

Person recognition from a ceiling camera

The EU project KSERA aims at helping elderly persons at home. For this purpose, a ceiling camera localises a helping robot and a person. To detect a person from a ceiling camera with a fisheye lense is challenging, because the person's appearance is very different seen from the top or from a side. We wish to implement a semisupervised classification algorithm for this task [e.g. 1].

Goals:

Implement a semi-supervised learning algorithm and test it on toy data.
Read videos collected from the ceiling camera with person(s) and robot. Handlabelling of a small subset of frames.
Possible use of Webots 3D simulator for video data of Nao robot.
Extraction of visual features and application and test of the learning algorithm.
Further develop the algorithm, e.g. implementation via a neural network.

Requirements:

Programming experience, preferably C/C++, Python. Possible use of packages, e.g.: OpenCV, www.pymix.org, www.shogun-toolbox.org.
Keen interest in artificial intelligence and bio-inspired methods.
Openness to new challenges and own initiative.

[1] Goal-Directed Feature Learning. Weber & Triesch (2009), www.informatik.uni-hamburg.de/~weber/publications/09IJCNN_RLfeatures_0660.pdf

For further questions, please contact any of the below.

Contact:

Professor Dr. Stefan Wermter, Dr. Cornelius Weber, Dipl.-Ing. Alex Yan

Robot Grasping

Intelligent robotics is a wide area ranging from bio-inspired learning, vision, motor control to household assistance. A central task that has been performed with robots but that still needs more development is to grasp an object. This thesis will tackle that task using the humanoid-shaped robot Nao and using learning neural networks. There are two sub-tasks:
(i) to align the robot close to the object by walking and
(ii) to grasp the object via arm movement and hand closure.
Each of these sub-tasks alone, successfully implemented, warrants one whole thesis

Goals:

Set up a Webots 3D simulator environment with Nao
Localise the object using visual features (one may use "Naomarks" markers)
For sub-task (i): adapt a reinforcement learning algorithm to control the robot to pose itself suitably in front of the object
For sub-task (ii): implement a neural network that associates a seen position of the object to a suitable arm pose

Useful Skills:

Programming experience, preferably Python, C/C++, OpenCV. Deeper knowledge will be aquired during the thesis
Keen interest in artificial intelligence, neural networks
Adaptiveness to new situations and new problem solving methodologies

Contact:

Dr. Cornelius Weber, Professor Dr. Stefan Wermter, Nicolas Navarro

Bio-inspired auditory pathway

Intelligent robotics is a wide area ranging from bio-inspired learning, vision, motor control to household assistance. We study and exploit nature-inspired and hybrid intelligence to build next-generation adaptive knowledge-based systems. A key factor is to move from simulation environments to embodied applications and architectures that present different kinds of challenges such as environmental noise and even ego-noise. In this project we want to investigate and develop a bio-inspired auditory model for one of our NAO robots. The NAO is a humanoid robot with four microphones in the head, a wide range of capabilities, and a sophisticated API.

Goals:

Create a data set of auditory stimuli in the context of a home-like environment.
Implement a bio-inspired auditory model. The model should include features such as a cochlear model, dynamic background noise filtering, ego-noise filtering, signal onset and end detection.
Optimize model for real-time applications targeting the NAO and iCub robot (either off-board or on-board processing).
The implementation should be done with re-use in mind, i.e. robust implementation, well-designed interface, good documentation, extensibility, possibly bindings for various programming languages/systems like ROS.

Useful skills:

Programming experience, preferably C/C++ and Python. Deeper knowledge will be acquired during the thesis
Keen interest in hybrid intelligent systems, applied artificial intelligence and robotics.
Adaptiveness to new situations and new problem solving methodologies.
Previous knowledge on digital signal processing techniques will be appreciated. Deeper knowledge will be acquired during the thesis.

Contact:

Professor Dr. Stefan Wermter, Dr. Cornelius Weber, Nicolas Navarro

Bio-inspired image stabilizing on a walking robot

Intelligent robotics ranges from bio-inspired learning, vision, motor control to household assistance. A key factor is to move from simulation environments to embodied applications, which presents different kinds of challenges such as noisy sensory signals and system perturbations. For example, the shaking video taken from a walking robot’s camera is unpleasant to watch, however, when we walk, we perceive a stable environment. In this project, we will develop a bio-inspired digital image stabilization system to be used on small-sized humanoid robots such as NAO and Darwin-OP. The thesis can first address work on a video and then on a robot in real-time.

Goals:

Implement a bio-inspired digital image stabilization system. The system should include features such as detection of ego-motion and compensate camera vibrations due to walking or other body movements.
Optimize the model for the Darwin-OP or the NAO robot for off-board and onboard processing.
The implementation should be done with re-use in mind, i.e. robust implementation, well-designed interface, good documentation, extensibility.

Useful skills:

Programming experience, preferably C/C++ and Python. Deeper knowledge will be acquired during the thesis.
Keen interest in hybrid intelligent systems, applied AI and robotics.
Previous knowledge on digital signal processing techniques will be appreciated. Deeper knowledge will be acquired during the thesis.

Contact:

Professor Dr. Stefan Wermter, Dr. Cornelius Weber, Nicolas Navarro

Neural network learning with a laser robot head

Being able to cover an indoor environment up to 4 meters, the laser head of NAO provides a compact and accurate method to obtain a 2D obstacle map for the use of humanoid robot navigation. Moreover, the laser data is integrated in NaoQi and can be easily retrieved by simple commands. This kind of laser head has been initially developed as a stair climbing robot, an obstacle avoidance robot, etc. In this project, we propose to use the laser sensor for moving object prediction in an ambient environment. While there is previous research [1] of dynamic collision avoidance or moving object tracking, we propose to realize these two aspects in a humanoid robot with neural networks technologies.

Goals:

Construct a 2D map with laser data in a real indoor environment.
For sub-task (i): Identify and track moving objects in the map, e.g. with a recurrent neural network.
For sub-task (ii): Build a collision avoidance model with neural network technologies.

Useful skills:

Programming experience, preferably Python, C/C++.
Keen interest in artificial intelligence and bio-inspired methods.
Collaboration with a technician for the laser head is possible.

Contact:

Professor Dr. Stefan Wermter, Dr. Cornelius Weber, Junpei Zhong

Completed Theses

Feb 2012	Grounding of Language in Sensorimotor World Interaction of a Humanoid Robot, using Neural Networks (BSc Thesis)	Thomas Christian Blank
Jan 2012	Evolving Neural Networks for Bipedal Walking of a NAO Robot (BSc Thesis)	Patrick Schmolke
Nov 2011	Geräuschquellenlokalisation mit einem humanoiden Roboter (BSc Thesis)	Robert Keßler