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teaching:gsoc2017 [2017/02/08 08:08] – [CRAM -- Robot Plans] liscateaching:gsoc2017 [2017/02/08 08:11] – [Topic 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios] lisca
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 use-cases can be found at the [[http://www.cram-system.org/|CRAM use-cases can be found at the [[http://www.cram-system.org/|CRAM
 website]]. website]].
 +
 +===== openEASE -- Experiment Knowledge Database =====
 +
 +OpenEASE is a generic knowledge database for collecting and analysing experiment data. Its foundation is the KnowRob knowledge processing system and ROS, enhanced by reasoning mechanisms and a web interface developed for inspecting comprehensive experiment logs. These logs can be recorded for example from complex CRAM plan executions, virtual reality experiments, or human tracking systems. OpenEASE offers interfaces for both, human researchers that want to visually inspect what has happened during a robot experiment, and robots that want to reason about previous task executions in order to improve their behavior.
 +
 +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]]
  
  
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