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teaching:gsoc2017 [2017/02/08 08:11] – [Topic 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios] liscateaching:gsoc2017 [2017/02/08 08:49] – [Topic 3: ROS with PR2 integration in Unreal Engine] lisca
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 {{  :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.
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 **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 be able to load URDF models of
 +varios robots (e.g. PR2) and be able to control them through ROS
 +in the game engine. In a similar fashion to a robotic simulator.
 +
 +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.
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 Contact: [[team/andrei_haidu|Andrei Haidu]] Contact: [[team/andrei_haidu|Andrei Haidu]]
  
 +==== Topic 4: Plan Library for Autonomous Robots performing Chemical Experiments ====
 +
 +{{  :teaching:gsoc:topic3_chem.png?nolink&200|}}
 +
 +**Main Objective:** of this theme is to develop in [[http://gazebosim.org/|Gazebo]] simulator a set of plan-based control programs which will equip an autonomous mobile robot to perform a set of typical manipulations within a chemistry laboratory. The set of plan-based control programs resulted at the end of the program will be tested on the real PR2 robot from the Institute for Artificial Intelligence of the University of Bremen, Germany.
 +
 +The successful candidate will use the domain specific language of [[http://cram-system.org/|CRAM]] toolbox and code plan-based control programs which will enable the PR2 robot to perform manipulations like: simple grasping
 +of different containers, screwing and unscrewing the cap of a test tube, pouring a substance from a container into another container, operating a centrifuge, etc.
 +
 +In the first phase of the project the successful candidate will make sure he/she is familiar with the domain specific language of CRAM toolbox and the parameters of the plan-based control programs. This phase will culminate with the student having coded a simple complete and fully runnable plan-based control program. 
 +
 +In the second phase of the project together with the successful candidate we will decide the set of manipulations which will be implemented in order to enable the robot to perform a simple and complete chemical experiment.
 +
 +In the last phase of the project, the plan-based control programs developed in the second phase will be put together and the complete chemical experiment will be tested and fixed until it runs successfully.
 +
 +The set of plan-based control programs resulted at the end of the program will represent the execution basis of the future experiments which will be done at IAI in order to figure out how an autonomous robot can reproduce a chemical experiment represented with semantic web tools.
 +
 +**Requirements:** The ideal candidate must be comfortable programming in LISP and familiar with the ROS concepts. The candidate familiar with the Gazebo simulator and CRAM toolbox will have a big plus.
 +
 +**Expected Results** We expect to successfully code a library of plan-based control programs which will enable an autonomous robot to manipulate the typical chemical laboratory equipment and perform a small class of chemical experiments in Gazebo simulator.
  
 +Contact: [[team/gheorghe_lisca|Gheorghe Lisca]]
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