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jobs [2015/06/19 12:58] – winkler | jobs [2017/02/02 11:46] – [Theses and Jobs] balintbe | ||
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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. \\ | |
- | == HiWi-Position: | + | 5 hours per week for up to 1 year (paid). |
- | + | ||
- | When dealing with real-world robot tasks, simulation that is close to reality is key to test behavior-driven, | + | |
Requirements: | Requirements: | ||
- | * Experience | + | * Good programming skills |
- | * Passion for Robotics | + | * Basic ROS knowledge |
- | * Ideally programming skills in Lisp, Prolog, and Java | + | |
- | Contact: [[team: | + | The student will be introduced to the CRAM framework at the beginning of the job, which is a robot programming framework written in Lisp. The student will then be responsible for assisting not familiar with the framework people, explaining them the parts they don't understand and pointing them to the relevant documentation sources. |
+ | Contact: [[team: | ||
+ | --> | ||
+ | </ | ||
- | == GPU-based Parallelization of Numerical Optimization Techniques (BA/ | ||
- | 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 | + | == Integrating PR2 in the Unreal Game Engine Framework |
+ | {{ : | ||
- | Requirements: | + | Integrating the [[https:// |
- | * Skills in numerical optimization algorithms | + | |
- | * Good programming skills in Python and C/C++ | + | |
- | + | ||
- | Contact: | + | |
- | + | ||
- | == Online Learning of Markov Logic Networks for Natural-Language Understanding (MA)== | + | |
- | + | ||
- | 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, | + | |
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]] |
- | ==HiWi-Position: | + | == Realistic Grasping using Unreal Engine (BA/ |
- | In the context of the European research project RoboHow.Cog [1,2] we | + | {{ : |
- | 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 | + | The objective of the project |
- | development and the integration of probabilistic methods | + | ious human-like grasping approaches |
- | This HiWi-Position can serve as a starting point for future Bachelor' | + | The game consist of a household environment where a user has to execute various given tasks, such as cooking |
- | Tasks: | + | In order to improve the ease of manipulating objects the user should |
- | * Implementation | + | be able to switch during runtime |
- | * Linkage of the knowledge | + | grasp, precision grip etc.) he/she would like to use. |
- | * Support for the scientific staff in extending and integrating components onto the robot platform PR2. | + | |
+ | Requirements: | ||
+ | * Good programming skills in C++ | ||
+ | * Good knowledge of the Unreal Engine API. | ||
+ | * Experience with skeletal control / animations / 3D models | ||
- | Requirements: | ||
- | * Studies in Computer Science (Bachelor' | ||
- | * Basic skills in Artificial Intelligence | ||
- | * Optional: basic skills in Probability Theory | ||
- | * Optional: basic skills in Machine Learning | ||
- | * Good programming skills in Python and Java | ||
- | |||
- | Hours: 10-20 h/week | ||
- | |||
- | Contact: [[team: | ||
- | |||
- | [1] www.robohow.eu\\ | ||
- | [2] http:// | ||
+ | Contact: [[team/ | ||
- | == Kitchen Activity Games in a Realistic Robotic Simulator (BA/MA/HiWi)== | + | == Kitchen Activity Games in a Realistic Robotic Simulator (BA/MA)== |
{{ : | {{ : | ||
Line 80: | Line 66: | ||
Contact: [[team: | Contact: [[team: | ||
- | |||
- | == Integrating Eye Tracking in the Kitchen Activity Games (BA/MA)== | ||
- | {{ : | ||
- | |||
- | Integrating the eye tracker in the [[http:// | ||
- | |||
- | Requirements: | ||
- | * Good programming skills in C/C++ | ||
- | * Gazebo simulator basic tutorials | ||
- | |||
- | Contact: [[team: | ||
- | |||
- | == Hand Skeleton Tracking Using Two Leap Motion Devices (BA/MA)== | ||
- | {{ : | ||
- | |||
- | Improving the skeletal tracking offered by the [[https:// | ||
- | |||
- | The tracked hand can then be used as input for the Kitchen Activity Games framework. | ||
- | |||
- | Requirements: | ||
- | * Good programming skills in C/C++ | ||
- | |||
- | Contact: [[team: | ||
- | |||
- | == Fluid Simulation in Gazebo (BA/MA)== | ||
- | {{ : | ||
- | |||
- | [[http:// | ||
- | |||
- | Currently there is an [[http:// | ||
- | |||
- | The computational method for the fluid simulation is SPH (Smoothed-particle Dynamics), however newer and better methods based on SPH are currently present | ||
- | and should be implemented (PCISPH/ | ||
- | |||
- | 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). | ||
- | |||
- | 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: | ||
- | * Good programming skills in C/C++ | ||
- | * Interest in Fluid simulation | ||
- | * Basic physics/ | ||
- | * 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 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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