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jobs [2014/06/17 09:05] – tenorth | jobs [2016/03/11 08:38] – KnowRob+MoveIt! bartelsg | ||
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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]] |
- | ==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 | + | |
+ | Requirements: | ||
+ | * Good programming skills in C++ | ||
+ | * Good knowledge of the Unreal Engine API. | ||
+ | * Experience with skeletal control / animations / 3D models | ||
+ | |||
+ | |||
+ | Contact: [[team/ | ||
+ | |||
+ | == Kitchen Activity Games in a Realistic Robotic Simulator (BA/MA)== | ||
+ | {{ : | ||
+ | |||
+ | Developing new activities | ||
Requirements: | Requirements: | ||
- | * Studies | + | * Good programming skills |
- | * Basic skills in Artificial Intelligence | + | * Basic physics/ |
- | * Optional: | + | * Gazebo simulator |
- | * Optional: basic skills in Machine Learning | + | |
- | * Good programming skills in Python and Java | + | |
- | Hours: 10-20 h/week | + | Contact: [[team: |
- | Contact: [[team: | ||
- | [1] www.robohow.eu\\ | ||
- | [2] http:// | ||
- | == Depth-Adaptive Superpixels | + | == Automated sensor calibration toolkit |
- | {{ : | + | |
- | 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 | + | Computer vision is an important part of autonomous robots. For robots the image sensors are the main source |
- | Requirements: | + | The topic for this thesis is to develop an automated system for calibrating cameras, especially RGB-D cameras like the Kinect v2. |
- | * Basic knowledge | + | |
- | * Good programming skills in C/C++. | + | {{ : |
- | * Experience | + | The system should: |
+ | * be independent | ||
+ | * 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://www.halcon.de/ | ||
+ | |||
+ | 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: | ||
+ | |||
+ | == Semantic Collision Checking for Planning Robot Manipulation Tasks == | ||
+ | {{ : | ||
+ | Service robots helping humans at home shall perform manipulation tasks like wiping a table or polishing glass surfaces. To successfully complete these tasks, robots needs to establish the ' | ||
+ | |||
+ | The goal of this project is to interface existing collision checking software from MoveIt! with the robot knowledge base KnowRob to enable semantic collision checking. As a result, the student will extend the KnowRob system by a couple of predicates which employ collision checking from MoveIt! to decide whether a given world state complies with a desired contact state. | ||
- | Contact: [[team: | + | Requirements: |
+ | * basic knowledge of ROS | ||
+ | * basic knowledge of robotics | ||
+ | * interest in using KnowRob and MoveIt! | ||
+ | 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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