Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision		Previous revision
Next revisionBoth sides next revision
teaching:gsoc2017 [2017/02/08 08:12] – [Topic 2: Realistic Grasping using Unreal Engine] liscateaching:gsoc2017 [2017/03/05 19:09] – [Topic 2: Realistic Grasping using Unreal Engine] ahaidu
Line 10: Line 10:
  
  
-**When contacting us, please make sure you read the description of the topic you are interested in carefully. Only contact the person responsible for the topic / topics you are interested in.  Please only ask topic-relevant specific questions, otherwise your emails will not be answered due to limited resources we have for processing the vast amount of GSoC inquiries. For more general questions please use our [[https://webchat.freenode.net/?channels=%23gsoc_ia|IRC channel]].**+**When contacting us, please make sure you read the description of the topic you are interested in carefully. Only contact the person responsible for the topic / topics you are interested in.  Please only ask topic-relevant specific questions, otherwise your emails will not be answered due to limited resources we have for processing the vast amount of GSoC inquiries. For more general questions please use [[https://gitter.im/iai_gsoc17/general|Gitter ]].**
  
 ===== KnowRob -- Robot Knowledge Processing ===== ===== KnowRob -- Robot Knowledge Processing =====
Line 78: Line 78:
 {{  :teaching:gsoc:topic1_rs.png?nolink&200|}} {{  :teaching:gsoc:topic1_rs.png?nolink&200|}}
  
-**Main Objective:** The main objective of this topic is to enable robots in a human environment to recognize objects in difficult and challenging scenarios. To achieve this the participant will develop software components for RoboSherlock, called annotators, that are aided by background knowledge in order to detect objects. These scenarios include stacked,occluded objects placed on shelves, objects in drawers, refrigerators, dishwashers, cupboards etc. In typical scenarios, these confined spaces also bare an underlying structure, which will be exploited, and used as background knowledge, to aid perception (e.g. stacked plates would show up as parallel lines using an +**Main Objective:** In this topic we will develop algorithms that en- 
-edge detection).+able robots in a human environment to recognize objects in diffi- 
 +cult and challenging scenarios. To achieve this the participant will 
 +develop annotators for RoboSherlock that are particularly aimed at 
 +object-hypotheses generation and merging. Generating a hypotheses 
 +essentially means to generate regions/clusters in our raw data that 
 +form a single object or object-part. In particular this entails the de- 
 +velopment of segmentation algorithms for visually challenging scenes 
 +or object properties, as the likes of transparent objects, or cluttered, 
 +occluded scenes. The addressed scenarios include stacked, occluded 
 +objects placed on shelves, objects in drawers, refrigerators, dishwash- 
 +ers, cupboards etc. In typical scenarios, these confined spaces also 
 +bare an underlying structure, which will be exploited, and used as 
 +background knowledge, to aid perception (e.g. stacked plates would 
 +show up as parallel lines using an edge detection). Specifically we 
 +would start from (but not necessarly limit ourselves to) the implemen- 
 +tation of two state-of-the-art algorithms described in recent papers: 
 +[1] Ilya Lysenkov, Victor Eruhimov, and Gary Bradski, Recognition 
 +and Pose Estimation of Rigid Transparent Objects with a Kinect Sen- 
 +sor, 2013 Robotics: Science and Systems Conference (RSS), 2013. 
 +[2] Richtsfeld A., M ̈ 
 +orwald T., Prankl J., Zillich M. and Vincze 
 +M. - Segmentation of Unknown Objects in Indoor Environments. 
 +IEEE/RSJ International Conference on Intelligent Robots and Sys- 
 +tems (IROS), 2012.
  
 **Task Difficulty:** The task is considered to be challenging, as it is still a hot research topic where general solutions do not exist. **Task Difficulty:** The task is considered to be challenging, as it is still a hot research topic where general solutions do not exist.
      
-**Requirements:** Good programming skills in C++ and basic knowledge of CMake. Experience with PCL, OpenCV is prefered. Knowledge of Prolog is a plus.+**Requirements:** Good programming skills in C++ and basic knowl- 
 +edge of CMake. Experience with PCL, OpenCV is prefered.
  
 **Expected Results:** Currently the RoboSherlock framework lacks good perception algorithms that can generate object-hypotheses in challenging scenarios(clutter and/or occlusion). The expected results are several software components based on recent advances in cluttered scene analysis that are able to successfully recognized objects in the scenarios mentioned in the objectives, or a subset of these. **Expected Results:** Currently the RoboSherlock framework lacks good perception algorithms that can generate object-hypotheses in challenging scenarios(clutter and/or occlusion). The expected results are several software components based on recent advances in cluttered scene analysis that are able to successfully recognized objects in the scenarios mentioned in the objectives, or a subset of these.
Line 106: Line 130:
      
 **Requirements:** Good programming skills in C++. Good knowledge of the Unreal Engine API. Experience with skeletal control / animations / 3D models in Unreal Engine. **Requirements:** Good programming skills in C++. Good knowledge of the Unreal Engine API. Experience with skeletal control / animations / 3D models in Unreal Engine.
 +
 +**Expected Results** We expect to enhance our currently developed robot learning game with realistic human-like grasping capabilities. These would allow users to interact more realistically with the given virtual environment. Having the possibility to manipulate objects of various shapes and sizes will allow to increase the repertoire of the executed tasks in the game. Being able to switch between specific grasps will allow us to learn grasping models specific to each manipulated object.
 +
 +Contact: [[team/andrei_haidu|Andrei Haidu]]
 +
 +==== Topic 3: ROS with PR2 integration in Unreal Engine ====
 +
 +{{ :teaching:unreal_ros_pr2.png?200|}}
 +
 +**Main Objective:** The objective of the project is to integrate the
 +PR2 8 robot in [[https://www.unrealengine.com/|Unreal Engine]] with [[https://ros.org|ROS]] support. Due to the lack of Windows OS support of ROS, this will be imple-mented in the Linux distribution of the engine.
 +
 +**Task Difficulty:** The task is to be placed in the medium difficulty
 +level, as it requires programming skills of various frameworks (ROS,
 +Linux, Unreal Engine).
 +  
 +**Requirements:** Good programming skills in C++. Good knowledge
 +of the Unreal Engine API, ROS and Linux. Some experience in
 +robotics.
  
 **Expected Results** We expect to enhance our currently developed robot learning game with realistic human-like grasping capabilities. These would allow users to interact more realistically with the given virtual environment. Having the possibility to manipulate objects of various shapes and sizes will allow to increase the repertoire of the executed tasks in the game. Being able to switch between specific grasps will allow us to learn grasping models specific to each manipulated object. **Expected Results** We expect to enhance our currently developed robot learning game with realistic human-like grasping capabilities. These would allow users to interact more realistically with the given virtual environment. Having the possibility to manipulate objects of various shapes and sizes will allow to increase the repertoire of the executed tasks in the game. Being able to switch between specific grasps will allow us to learn grasping models specific to each manipulated object.
- Impressum - Datenschutz



Prof. Dr. hc. Michael Beetz PhD
Head of Institute

Contact via
Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de

Discover our VRB for innovative and interactive research


Memberships and associations:


Social Media: