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teaching:gsoc2017 [2017/02/08 08:08] – [CRAM -- Robot Plans] liscateaching:gsoc2017 [2017/02/08 08:12] – [Topic 2: Realistic Grasping using Unreal Engine] lisca
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 The OpenEASE web interface as well as further information and publication material can be accessed through its publicly available [[http://www.open-ease.org/|website]]. It is meant to make complex experiment data available to research fields adjacent to robotics, and to foster an intuition about robot experience data. The OpenEASE web interface as well as further information and publication material can be accessed through its publicly available [[http://www.open-ease.org/|website]]. It is meant to make complex experiment data available to research fields adjacent to robotics, and to foster an intuition about robot experience data.
  
 +===== RoboSherlock -- Framework for Cognitive Perception =====
  
 +RoboSherlock is a common framework for cognitive perception, based on the principle of unstructured information management (UIM). UIM has proven itself to be a powerful paradigm for scaling intelligent information and question answering systems towards real-world complexity (i.e. the Watson system from IBM). Complexity in UIM is handled by identifying (or hypothesizing) pieces of
 +structured information in unstructured documents, by applying ensembles of experts for annotating information pieces, and by testing and integrating these isolated annotations into a comprehensive interpretation of the document.
 +
 +RoboSherlock builds on top of the ROS ecosystem and is able to wrap almost any existing perception algorithm/framework, and allows easy and coherent combination of the results of these. The framework has a close integration with two of the most popular libraries used in robotic perception, namely OpneCV and PCL. More details about RoboSherlock can be found on the project [[http://robosherlock.org/|webpage]].
 +
 +===== Proposed Topics =====
 +
 +In the following, we list our proposals for the Google Summer of Code topics that contribute to the aforementioned
 +open-source projects.
 +
 +==== Topic 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios ====
 +
 +{{  :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
 +edge detection).
 +
 +**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.
 +
 +**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.
 +
 +Contact: [[team/ferenc_balint-benczedi|Ferenc Bálint-Benczédi]]
 +
 +==== Topic 2: Realistic Grasping using Unreal Engine ====
 +
 +{{  :teaching:gsoc:topic2_unreal.png?nolink&200|}}
 +
 +**Main Objective:** The objective of the project is to implement var-
 +ious human-like grasping approaches in a game developed using [[https://www.unrealengine.com/|Unreal Engine]]. 
 +
 +The game consist of a household environment where a user has to execute various given tasks, such as cooking a dish, setting the table, cleaning the dishes etc. The interaction is done using various sensors to map the users hands onto the virtual hands in the game.
 +
 +In order to improve the ease of manipulating objects the user should
 +be able to switch during runtime the type of grasp (pinch, power
 +grasp, precision grip etc.) he/she would like to use.
 +
 +**Task Difficulty:** The task is to be placed in the easy difficulty
 +level, as it requires less algorithmic knowledge and more programming skills.
 +  
 +**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 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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