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teaching:gsoc2014 [2014/03/06 11:43]
tenorth [Google Summer of Code 2014]
teaching:gsoc2014 [2014/07/08 11:23]
tenorth [KnowRob -- Robot Knowledge Processing]
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 of applications,​ from understanding instructions from the Web (RoboHow), of applications,​ from understanding instructions from the Web (RoboHow),
 describing multi-robot search-and-rescue tasks (SHERPA), assisting elderly describing multi-robot search-and-rescue tasks (SHERPA), assisting elderly
-people in their homes (SRS) to industrial assembly tasks (SMERobotics).+people in their homes (SRS) to industrial assembly tasks ([[http://​www.smerobotics.org|SMErobotics]]).
  
 KnowRob is an open-source project hosted at [[http://​github.com/​knowrob|GitHub]] KnowRob is an open-source project hosted at [[http://​github.com/​knowrob|GitHub]]
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 ==== Topic 2: CRAM -- Symbolic Reasoning Tools with Bullet ==== ==== Topic 2: CRAM -- Symbolic Reasoning Tools with Bullet ====
 {{  :​teaching:​gsoc:​handle_detection_2.png?​nolink&​200|}} {{  :​teaching:​gsoc:​handle_detection_2.png?​nolink&​200|}}
-**Main Objective: **Mapping the environment to the internal belief state representation and keeping track of changes in the environment to keep the belief state up to date based on manipulation and interaction tasks performed by the robot.\\ ​  +**Main Objective:​** Mapping the environment to the internal belief state representation and keeping track of changes in the environment to keep the belief state up to date based on manipulation and interaction tasks performed by the robot.\\ 
-**Task Difficulty:​** Relatively simple, when making the existing track changes more robust, and more challenging when introducing new change tracking ​(like noting the angle of open doors after opening them and storing it in the belief state).\\  +Possible sub-projects:​ (*) reflecting in the belief state the changes in the environment such as "a drawer has been opened"​ (up to storing the precise angle the door is at after opening), (*) assuring the physical consistency of the belief state, i.e. correcting explicitly wrong data coming from sensors to closest logically sound values, (*) improving the representation of previously unseen objects in the belief state by, e.g. interpolating the point cloud into a valid 3D mesh, (*) improving the visualization of the belief state and the intentions of the robot such as, e.g., visualizing the navigation goals the robot generated before starting a navigation task, (*) many other ideas that we can discuss based on applicants'​ individual interests and abilities.\\ 
 +**Task Difficulty:​** Relatively simple, when making the existing track changes more robust, and more challenging when introducing new change tracking.\\
 {{  :​teaching:​gsoc:​pr2_dishwasher.jpg?​nolink&​200|}} {{  :​teaching:​gsoc:​pr2_dishwasher.jpg?​nolink&​200|}}
 **Requirements:​** At least basic understanding in functional programming is advisable (ideally Lisp), basic knowledge in ROS helps. Also a good understanding of geometric shapes and coordinate transformations helps.\\ **Requirements:​** At least basic understanding in functional programming is advisable (ideally Lisp), basic knowledge in ROS helps. Also a good understanding of geometric shapes and coordinate transformations helps.\\
 **Expected Results:** We expect operational and robust contributions to the software library that can be used as a part of robot'​s control program. **Expected Results:** We expect operational and robust contributions to the software library that can be used as a part of robot'​s control program.
 +
 +For more information consult the [[http://​cram-system.org/​doc/​reasoning/​overview|documentation]].
  
 Contact: [[team/​gayane_kazhoyan|Gayane Kazhoyan]] Contact: [[team/​gayane_kazhoyan|Gayane Kazhoyan]]