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:gsoc2018 [2018/01/21 20:44] – [Topic 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios] balintbeteaching:gsoc2018 [2018/01/22 13:03] – [CRAM - Cognition-enabled Robot Executive] gkazhoya
Line 37: Line 37:
  
 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]]. 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]].
 +
 +===== openEASE -- Web-based Robot Knowledge Service =====
 +
 +OpenEASE is a generic knowledge database for collecting and analyzing 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.
 +
 +===== RobCoG - Robot Commonsense Games =====
 +
 +[[http://robcog.org/|RobCoG]] (**Rob**ot **Co**mmonsense **G**ames) is a framework consisting of various open source games and plugins (https://github.com/robcog-iai) written in the Unreal Engine with the intention of collecting and equipping robots with commonsense and naive physics knowledge. Various Game prototypes are created where users are asked to execute kitchen related tasks. During gameplay developed game plugins automatically collecting symbolic and sub-symbolic data. The automatically annotated data is then stored in the web-based knowledge service [[http://www.open-ease.org/|openEASE]]. This allows robots to access it and reason about it.
 +
 +The games are split into two categories: (1) VR/Full Body Tracking with physics based interactions, where data as close as possible to reality is collected. The users are immersed in a virtual environment and are asked to perform tasks using natural movements. (2) FPS style, web-based games, where the users interact with objects using a keyboard and mouse. This allows for easy crowdsourcing capabilities since these games could be run from a browser (http://open-ease.org/robcogweb). The data will be less precise for more low level learning, however still valuable at a more higher level (e.g. positioning of objects, the order of executing actions etc.).
 +
 +
 +===== CRAM - Cognition-enabled Robot Executive =====
 +
 +CRAM is a software toolbox for the design, implementation and deployment of cognition-enabled plan execution on autonomous robots. CRAM equips autonomous robots with lightweight reasoning mechanisms that can infer control decisions rather than requiring the decisions to be preprogrammed. This way CRAM-programmed autonomous robots are more flexible and general than control programs that lack such cognitive capabilities. CRAM does not require the whole reasoning domain to be stated explicitly in an abstract knowledge base. Rather, it grounds symbolic expressions into the perception and actuation routines and into the essential data structures of the control plans. CRAM includes a domain-specific language that makes writing reactive concurrent robot behavior easier for the programmer. It extensively uses the ROS middleware infrastructure.
 +
 +CRAM is an open-source project hosted on [[https://github.com/cram2/cram|GitHub.]] It has its own
 +[[http://cram-system.org|project page]] that provides extensive documentation 
 +and tutorials that help to get started.
  
  
Line 90: Line 111:
 Contact: [[team/ferenc_balint-benczedi|Ferenc Bálint-Benczédi]] Contact: [[team/ferenc_balint-benczedi|Ferenc Bálint-Benczédi]]
  
 +==== Topic 3: Unreal - ROS 2 Integration ====
 +
 +{{  :teaching:gsoc:ue_ros2.png?nolink&200|}}
 +
 +TODO
 +
 +**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. Experience with ROS, ROS 2, c++ library linkage in Unreal Engine.
 +
 +**Expected Results** We expect to have an integrated communication level with ROS 2 and Unreal Engine on Windows and Linux side.
 +
 +Contact: [[team/andrei_haidu|Andrei Haidu]]
 +
 +
 +==== Topic 4: Unreal Editor User Interface Development ====
 +
 +{{  :teaching:gsoc:ue_editor.png?nolink&200|}}
 +
 +TODO
 +
 +**Task Difficulty:** The task is to be placed in the easy difficulty level, as it only requires familiarity with the [[https://docs.unrealengine.com/latest/INT/Programming/Slate/|SLATE]] framework from Unreal Engine.
 +  
 +**Requirements:** Good programming skills in C++. Good knowledge of the Unreal Engine API. Experience with the [[https://docs.unrealengine.com/latest/INT/Programming/Slate/|SLATE]] framework.
 +
 +**Expected Results** We expect to have intuitive Unreal Engine UI Panels for editing, visualizing various RobCoG plugins data and features.
 +
 +Contact: [[team/andrei_haidu|Andrei Haidu]]
 +
 +
 +==== Topic 5: Unreal - openEASE Live Connection ====
 +
 +{{  :teaching:gsoc:ue_oe.png?nolink&200|}}
 +
 +TODO
 +
 +**Task Difficulty:** The task is to be placed in the medium difficulty level, as it required knowledge of various frameworks/libraries (Unreal Engine, openEASE, c++ websocket communication)
 +  
 +**Requirements:** Good programming skills in C++. Good knowledge of the Unreal Engine API. Experience with c++ websocket based communication.
 +
 +**Expected Results** We expect to have a live connection with between openEASE and the Unreal Engine editor.
  
 +Contact: [[team/andrei_haidu|Andrei Haidu]], [[team/asil_kaan_bozcuoglu|Asil Kaan Bozcuoğlu]]
- 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: