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jobs [2014/06/17 09:05] – tenorth | jobs [2015/09/08 12:24] – [Theses and Jobs] nyga | ||
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- | == GPU-based Parallelization of Numerical Optimization Techniques | + | == Kitchen Activity Games in a Realistic Robotic Simulator |
+ | {{ : | ||
- | 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 | + | Developing new activities and improving |
Requirements: | Requirements: | ||
- | | + | * Good programming skills in C/C++ |
- | | + | * Basic physics/ |
+ | * Gazebo simulator basic tutorials | ||
- | Contact: [[team:daniel_nyga|Daniel Nyga]] | + | Contact: [[team:andrei_haidu|Andrei Haidu]] |
- | == Online Learning of Markov Logic Networks for Natural-Language Understanding | + | == Integrating Eye Tracking in the Kitchen Activity Games (BA/MA)== |
+ | {{ : | ||
- | Markov Logic Networks (MLNs) combine | + | Integrating |
Requirements: | Requirements: | ||
- | * Experience | + | * Good programming skills |
- | * Experience with statistical relational learning (e.g. MLNs) is helpful. | + | * Gazebo simulator basic tutorials |
- | * Good programming skills in Python. | + | |
- | Contact: [[team:daniel_nyga|Daniel Nyga]] | + | Contact: [[team:andrei_haidu|Andrei Haidu]] |
+ | == Hand Skeleton Tracking Using Two Leap Motion Devices (BA/MA)== | ||
+ | {{ : | ||
- | ==HiWi-Position: Knowledge Representation & Language Understanding for Intelligent Robots== | + | Improving the skeletal tracking offered by the [[https:// |
- | In the context of the European research project RoboHow.Cog [1,2] we | + | The tracked hand can then be used as input for the Kitchen Activity Games framework. |
- | 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 like cooking meals or straightening up rooms. | + | |
- | The Institute for Artificial Intelligence is hiring a student researcher for the | + | Requirements: |
- | development and the integration of probabilistic methods | + | * Good programming skills |
- | This HiWi-Position can serve as a starting point for future Bachelor' | + | Contact: [[team: |
- | Tasks: | + | == Fluid Simulation in Gazebo (BA/MA)== |
- | * Implementation of an interface to the Robot Operating System | + | |
- | * Linkage | + | |
- | * Support | + | [[http:// |
+ | |||
+ | Currently there is an [[http:// | ||
+ | |||
+ | The computational method | ||
+ | and should be implemented (PCISPH/ | ||
+ | |||
+ | The interaction between | ||
+ | |||
+ | Another topic would be the visualization of the fluid, currently is done by rendering every particle. For the rendering engine [[http:// | ||
+ | |||
+ | Here is a [[https:// | ||
Requirements: | Requirements: | ||
- | * Studies in Computer Science (Bachelor' | + | * Good programming |
- | * Basic skills in Artificial Intelligence | + | * Interest |
- | * Optional: basic skills | + | * Basic physics/ |
- | * Optional: basic skills in Machine Learning | + | * Gazebo simulator |
- | * Good programming skills in Python | + | |
- | Hours: 10-20 h/week | + | Contact: [[team: |
- | Contact: [[team: | ||
- | [1] www.robohow.eu\\ | + | == Automated sensor calibration toolkit (BA/MA)== |
- | [2] http:// | + | |
+ | 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, | ||
- | == Depth-Adaptive Superpixels (BA/ | + | The topic for this thesis is to develop an automated system for calibrating cameras, especially |
- | {{ : | + | |
- | 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 | + | |
- | Since the current implementation | + | {{ : |
+ | 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: | + | Requirements: |
- | * Basic knowledge of image processing | + | * Good programming skills in Python and C/C++ |
- | * Good programming skills in C/C++. | + | * ROS, OpenCV |
- | * Experience with CUDA is helpful | + | |
+ | [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++ | ||
+ | * strong background in computer vision | ||
+ | * ROS, OpenCV, PCL | ||
+ | |||
+ | Contact: [[team: | ||
+ | |||
+ | == Simulation of a robots belief state to support perception(MA) == | ||
+ | |||
+ | Create a simulation environment that represents the robots current belief state and can be updated frequently. Use off-screen rendering to investigate the affordances these objects possess, in order to support segmentation, | ||
+ | |||
+ | Requirements: | ||
+ | * Good programming skills in C/C++ | ||
+ | * strong background in computer vision | ||
+ | * Gazebo, OpenCV, PCL | ||
+ | |||
+ | Contact: [[team: | ||
- | Contact: [[team:jan-hendrik_worch|Jan-Hendrik Worch]] | + | == 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++ | ||
+ | * strong background in 3D vision | ||
+ | * basic knowledge of ROS, OpenCV, PCL | ||
+ | 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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