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jobs [2014/08/29 09:33] – [Theses and Jobs] ahaidu | jobs [2016/02/24 13:16] – gkazhoya | ||
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If you are looking for a bachelor/ | If you are looking for a bachelor/ | ||
+ | == Lisp / CRAM support assistant (HiWi) == | ||
+ | Technical support for the group for Lisp and the CRAM framework. \\ | ||
+ | 5 hours per week for up to 1 year (paid). | ||
- | == GPU-based Parallelization of Numerical Optimization Techniques (BA/ | + | Requirements: |
+ | * Good programming skills in Common Lisp | ||
+ | * Basic ROS knowledge | ||
- | In the field of Machine Learning, numerical optimization techniques play a focal role. However, as models grow larger, traditional implementations on single-core CPUs suffer from sequential execution causing a severe slow-down. In this thesis, state-of-the-art GPU frameworks (e.g. CUDA) are to be investigated in order implement numerical optimizers that substantially profit from parallel execution. | + | The student will be introduced to the CRAM framework at the beginning |
- | Requirements: | + | Contact: [[team: |
- | * Skills in numerical optimization algorithms | + | |
- | * Good programming skills in Python and C/C++ | + | |
- | Contact: [[team: | ||
- | == Online Learning of Markov Logic Networks for Natural-Language Understanding | + | == Integrating PR2 in the Unreal Game Engine Framework |
+ | {{ : | ||
- | Markov Logic Networks (MLNs) combine | + | Integrating |
Requirements: | Requirements: | ||
- | * Experience | + | * Good programming skills |
- | * Experience with statistical relational learning (e.g. MLNs) is helpful. | + | * Basic physics/ |
- | * Good programming skills in Python. | + | * Basic ROS knowledge |
+ | * UE4 basic tutorials | ||
- | Contact: [[team:daniel_nyga|Daniel Nyga]] | + | Contact: [[team:andrei_haidu|Andrei Haidu]] |
+ | == Kitchen Activity Games in a Realistic Robotic Simulator (BA/MA)== | ||
+ | {{ : | ||
- | ==HiWi-Position: Knowledge Representation & Language Understanding | + | Developing new activities and improving the current simulation framework done under the [[http:// |
- | In the context of the European research project RoboHow.Cog [1,2] we | + | Requirements: |
- | are investigating methods for combining multimodal sources of knowledge (e.g. video, natural-language recipes or computer games), in order to enable mobile robots to autonomously acquire new high level skills | + | * Good programming |
+ | * Basic physics/ | ||
+ | * Gazebo simulator basic tutorials | ||
- | The Institute for Artificial Intelligence is hiring a student researcher for the | + | Contact: [[team: |
- | development and the integration of probabilistic methods in AI, which enable intelligent robots to understand, interpret and execute natural-language instructions from recipes from the World Wide Web. | + | |
- | This HiWi-Position can serve as a starting point for future Bachelor' | + | == Integrating Eye Tracking in the Kitchen Activity Games (BA/MA)== |
+ | {{ : | ||
- | Tasks: | + | Integrating the eye tracker in the [[http:// |
- | * Implementation of an interface to the Robot Operating System (ROS). | + | |
- | * Linkage | + | |
- | * Support for the scientific staff in extending and integrating components onto the robot platform PR2. | + | |
Requirements: | Requirements: | ||
- | * Studies in Computer Science (Bachelor' | + | * Good programming |
- | * Basic skills in Artificial Intelligence | + | * Gazebo simulator |
- | * Optional: | + | |
- | * Optional: basic skills in Machine Learning | + | |
- | * Good programming skills in Python and Java | + | |
- | Hours: 10-20 h/week | + | Contact: [[team: |
- | Contact: [[team:daniel_nyga|Daniel Nyga]] | + | == Hand Skeleton Tracking Using Two Leap Motion Devices (BA/MA)== |
+ | | ||
- | [1] www.robohow.eu\\ | + | Improving the skeletal tracking offered by the [[https://developer.leapmotion.com/|Leap Motion SDK]], by using two devices (one tracking vertically the other horizontally) and switching between them to the one that has the best current view of the hand. |
- | [2] http://www.youtube.com/watch? | + | |
- | + | The tracked hand can then be used as input for the Kitchen Activity Games framework. | |
- | == Depth-Adaptive Superpixels (BA/MA)== | + | |
- | {{ : | + | |
- | We are currently investigating a new set of sensors (RGB-D-T), which is a combination of a kinect with a thermal image camera. Within this project we want to enhance the Depth-Adaptive Superpixels (DASP) to make use of the thermal sensor data. Depth-Adaptive Superpixels oversegment an image taking into account the depth value of each pixel. | + | |
- | + | ||
- | Since the current implementation of DASP is not very performant for high resolution images, there are several options for doing a project in this field like reimplementing DASP using CUDA, investigating how thermal data can be integrated, ... | + | |
Requirements: | Requirements: | ||
- | | + | * Good programming skills in C/C++ |
- | | + | |
- | * Experience with CUDA is helpful | + | |
- | Contact: [[team:jan-hendrik_worch|Jan-Hendrik Worch]] | + | Contact: [[team:andrei_haidu|Andrei Haidu]] |
+ | == Fluid Simulation in Gazebo (BA/MA)== | ||
+ | {{ : | ||
- | == Physical Simulation of Humans (BA/MA)== | + | [[http://gazebosim.org/|Gazebo]] currently only supports rigid body physics engines (ODE, Bullet etc.), however in some cases fluids are preferred in order to simulate as realistically as possible the given environment. |
- | | + | |
- | For tracking people, the use of particle filters | + | Currently there is an [[http:// |
- | Requirements: | + | The computational method for the fluid simulation is SPH (Smoothed-particle Dynamics), however newer and better methods based on SPH are currently present |
- | * Good programming skills in C/C++ | + | and should be implemented (PCISPH/IISPH). |
- | * Optional: Experience in working with physics libraries such as Bullet | + | |
- | Contact: [[team: | + | The interaction between the fluid and the rigid objects is a naive one, the forces and torques are applied only from the particle collisions (not taking into account pressure and other forces). |
- | == Kitchen Activity Games in a Realistic Robotic Simulator (BA/MA/HiWi)== | + | Another topic would be the visualization of the fluid, currently is done by rendering every particle. For the rendering engine [[http://www.ogre3d.org/|OGRE]] is used. |
- | {{ : | + | |
- | Developing new activities and improving | + | Here is a [[https:// |
Requirements: | Requirements: | ||
* Good programming skills in C/C++ | * Good programming skills in C/C++ | ||
+ | * Interest in Fluid simulation | ||
* Basic physics/ | * Basic physics/ | ||
- | * Gazebo simulator basic tutorials | + | * Gazebo simulator |
Contact: [[team: | Contact: [[team: | ||
- | == Integrating Eye Tracking in the Kitchen Activity Games (BA/MA)== | ||
- | {{ : | ||
- | Integrating the eye tracker in the Gazebo based Kitchen Activity Games framework | + | == Automated sensor calibration toolkit (BA/MA)== |
- | and logging the gaze of the user during the gameplay. From the information typical activities should be inferred. | + | |
- | Requirements: | + | Computer vision is an important part of autonomous robots. For robots the image sensors are the main source of information of the surrounding world. Each camera is different, even if they are from the same production line. For computer vision, especially for robots manipulating their environment, |
+ | |||
+ | The topic for this thesis is to develop an automated system for calibrating cameras, especially RGB-D cameras like the Kinect v2. | ||
+ | |||
+ | {{ : | ||
+ | The system should: | ||
+ | * be independent of the camera type | ||
+ | * estimate intrinsic and extrinsic parameters | ||
+ | * calibrate depth images (case of RGB-D) | ||
+ | * integrate capabilities from Halcon [1] | ||
+ | * operate autonomously | ||
+ | |||
+ | Requirements: | ||
+ | * Good programming skills in Python and C/C++ | ||
+ | * ROS, OpenCV | ||
+ | |||
+ | [1] http:// | ||
+ | |||
+ | Contact: [[team: | ||
+ | |||
+ | == On-the-fly 3D CAD model creation (MA)== | ||
+ | |||
+ | Create models during runtime for unknown textured objets based on depth and color information. Track the object and update the model with more detailed information, | ||
+ | |||
+ | Requirements: | ||
* Good programming skills in C/C++ | * Good programming skills in C/C++ | ||
- | * Gazebo simulator basic tutorials | + | * strong background in computer vision |
+ | * ROS, OpenCV, PCL | ||
- | Contact: [[team:andrei_haidu|Andrei Haidu]] | + | Contact: [[team:thiemo_wiedemeyer|Thiemo Wiedemeyer]] |
- | == Hand Skeleton Tracking Using Two Leap Motion Devices | + | == Simulation of a robots belief state to support perception(MA) == |
- | {{ : | + | |
- | Improving | + | Create a simulation environment that represents |
- | The tracked hand can then be used as input for the Kitchen Activity Games framework. | + | Requirements: |
+ | * Good programming skills in C/C++ | ||
+ | * strong background in computer vision | ||
+ | * Gazebo, OpenCV, PCL | ||
- | Requirements: | + | Contact: [[team: |
+ | |||
+ | == Multi-expert segmentation of cluttered and occluded scenes == | ||
+ | |||
+ | Objects in a human environment are usually found in challenging scenes. They can be stacked upon eachother, touching or occluding, can be found in drawers, cupboards, refrigerators and so on. A personal robot assistant in order to execute a task, needs to detect these objects and recognize them. In this thesis a multi-modal approach to interpreting cluttered scenes is going to be investigated, | ||
+ | |||
+ | Requirements: | ||
* Good programming skills in C/C++ | * Good programming skills in C/C++ | ||
+ | * strong background in 3D vision | ||
+ | * basic knowledge of ROS, OpenCV, PCL | ||
+ | |||
+ | Contact: [[team: | ||
+ | |||
- | Contact: [[team: |
Prof. Dr. hc. Michael Beetz PhD
Head of Institute
Contact via
Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de
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