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jobs [2014/06/17 09:05] – tenorth | 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 | + | The student will be introduced to the CRAM framework at the beginning |
+ | |||
+ | Contact: [[team: | ||
+ | |||
+ | |||
+ | == Integrating PR2 in the Unreal Game Engine Framework | ||
+ | {{ : | ||
+ | |||
+ | Integrating the [[https:// | ||
Requirements: | Requirements: | ||
- | | + | * Good programming skills in C/C++ |
- | | + | * Basic physics/ |
+ | * Basic ROS knowledge | ||
+ | * UE4 basic tutorials | ||
- | Contact: [[team:daniel_nyga|Daniel Nyga]] | + | Contact: [[team:andrei_haidu|Andrei Haidu]] |
- | == Online Learning of Markov Logic Networks for Natural-Language Understanding | + | == Kitchen Activity Games in a Realistic Robotic Simulator |
+ | {{ : | ||
- | Markov Logic Networks (MLNs) combine the expressive power of first-order logic and probabilistic graphical models. In the past, they have been successfully applied to the problem of semantically interpreting and completing natural-language instructions from the web. State-of-the-art learning techniques mostly operate in batch mode, i.e. all training instances need to be known in the beginning of the learning process. In context of this thesis, online learning methods for MLNs are to be investigated, which allow incremental learning, when new examples come in one-by-one. | + | Developing new activities |
Requirements: | Requirements: | ||
- | * Experience | + | * Good programming skills |
- | * Experience with statistical relational learning (e.g. MLNs) is helpful. | + | * Basic physics/ |
- | * Good programming skills in Python. | + | * Gazebo simulator basic tutorials |
- | Contact: [[team:daniel_nyga|Daniel Nyga]] | + | Contact: [[team:andrei_haidu|Andrei Haidu]] |
+ | == Integrating Eye Tracking in the Kitchen Activity Games (BA/MA)== | ||
+ | {{ : | ||
- | ==HiWi-Position: Knowledge Representation & Language Understanding for Intelligent Robots== | + | Integrating the eye tracker in 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 |
+ | * 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' | + | == Hand Skeleton Tracking Using Two Leap Motion Devices (BA/MA)== |
+ | {{ : | ||
- | Tasks: | + | Improving the skeletal tracking offered by the [[https:// |
- | * Implementation of an interface to the Robot Operating System (ROS). | + | |
- | * Linkage of the knowledge base to the executive | + | The tracked hand can then be used as input for the Kitchen Activity Games framework. |
- | * Support | + | |
Requirements: | Requirements: | ||
- | | + | * Good programming skills in C/C++ |
- | * Basic skills in Artificial Intelligence | + | |
- | * Optional: basic skills in Probability Theory | + | |
- | * Optional: basic skills in Machine Learning | + | |
- | | + | |
- | Hours: 10-20 h/week | + | Contact: [[team: |
- | Contact: [[team:daniel_nyga|Daniel Nyga]] | + | == Fluid Simulation in Gazebo (BA/MA)== |
+ | | ||
- | [1] www.robohow.eu\\ | + | [[http://gazebosim.org/ |
- | [2] http://www.youtube.com/ | + | |
+ | Currently there is an [[http:// | ||
- | == Depth-Adaptive Superpixels (BA/HiWi)== | + | The computational method for the fluid simulation is SPH (Smoothed-particle Dynamics), however newer and better methods based on SPH are currently |
- | {{ : | + | and should be implemented |
- | We are currently | + | |
- | 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, ... | + | 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: | ||
- | * Basic knowledge of image processing | + | |
- | * Good programming skills in C/C++. | + | * Interest in Fluid simulation |
- | * Experience | + | |
+ | * Gazebo simulator and Fluidix basic tutorials | ||
+ | |||
+ | Contact: [[team: | ||
+ | |||
+ | |||
+ | == Automated sensor calibration toolkit (BA/MA)== | ||
+ | |||
+ | 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++ | ||
+ | * 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: | ||
+ | |||
+ | == 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 | ||
- | Contact: [[team: | + | 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
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ai-office@cs.uni-bremen.de
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