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teaching:gsoc2018 [2018/01/17 18:44] – nyga | teaching:gsoc2018 [2018/01/23 08:33] – [Google Summer of Code 2018] ahaidu | ||
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====== Google Summer of Code 2018 ====== | ====== Google Summer of Code 2018 ====== | ||
~~NOTOC~~ | ~~NOTOC~~ | ||
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+ | In the following we shortly present the open source frameworks that are participating for this year's Google Summer of Code. | ||
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+ | For the **proposed topics** see [[# | ||
===== pracmln ===== | ===== pracmln ===== | ||
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and tutorials that facilitate getting started with MLNs. It is provided as a pip | and tutorials that facilitate getting started with MLNs. It is provided as a pip | ||
package in the Python package index ([[https:// | package in the Python package index ([[https:// | ||
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+ | ===== RoboSherlock -- Framework for Cognitive Perception ===== | ||
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+ | 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. | ||
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+ | RoboSherlock builds on top of the ROS ecosystem and is able to wrap almost any existing perception algorithm/ | ||
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+ | ===== openEASE -- Web-based Robot Knowledge Service ===== | ||
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+ | 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, | ||
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+ | The OpenEASE web interface as well as further information and publication material can be accessed through its publicly available [[http:// | ||
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+ | ===== RobCoG - Robot Commonsense Games ===== | ||
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+ | [[http:// | ||
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+ | The games are split into two categories: (1) VR/Full Body Tracking with physics based interactions, | ||
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+ | ===== CRAM - Cognition-enabled Robot Executive ===== | ||
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+ | 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:// | ||
+ | [[http:// | ||
+ | and tutorials that help to get started. | ||
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===== Proposed Topics ===== | ===== Proposed Topics ===== | ||
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**Contact: | **Contact: | ||
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+ | ==== Topic 2: Felxible perception pipeline manipulation for RoboSherlock ==== | ||
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+ | {{ : | ||
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+ | **Main Objective: | ||
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+ | **Task Difficulty: | ||
+ | | ||
+ | **Requirements: | ||
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+ | **Expected Results:** an extension to RoboShelrock that allows splitting and joingin pipelines, executing them in parallel, merging results from multiple types of cameras etc. | ||
+ | |||
+ | Contact: [[team/ | ||
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+ | ==== Topic 3: Unreal - ROS 2 Integration ==== | ||
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+ | {{ : | ||
+ | |||
+ | TODO | ||
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+ | **Task Difficulty: | ||
+ | | ||
+ | **Requirements: | ||
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+ | **Expected Results** We expect to have an integrated communication level with ROS 2 and Unreal Engine on Windows and Linux side. | ||
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+ | Contact: [[team/ | ||
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+ | |||
+ | ==== Topic 4: Unreal Editor User Interface Development ==== | ||
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+ | {{ : | ||
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+ | TODO | ||
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+ | **Task Difficulty: | ||
+ | | ||
+ | **Requirements: | ||
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+ | **Expected Results** We expect to have intuitive Unreal Engine UI Panels for editing, visualizing various RobCoG plugins data and features. | ||
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+ | Contact: [[team/ | ||
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+ | ==== Topic 5: Unreal - openEASE Live Connection ==== | ||
+ | |||
+ | {{ : | ||
+ | |||
+ | TODO | ||
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+ | **Task Difficulty: | ||
+ | | ||
+ | **Requirements: | ||
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+ | **Expected Results** We expect to have a live connection with between openEASE and the Unreal Engine editor. | ||
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+ | Contact: [[team/ | ||
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+ | |||
+ | ==== Topic 6: CRAM -- Visualizing Robot' | ||
+ | |||
+ | {{ : | ||
+ | |||
+ | **Main Objective: | ||
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+ | **Task Difficulty: | ||
+ | |||
+ | |||
+ | {{ : | ||
+ | |||
+ | **Requirements: | ||
+ | * Familiarity with functional programming paradigms: some functional programming experience is a requirement (preferred language is Lisp but Haskel, Scheme, OCaml, Clojure, Scala or similar will do); | ||
+ | * Experience with ROS (Robot Operating System). | ||
+ | |||
+ | **Expected Results:** We expect operational and robust contributions to the source code of the existing robot control system including documentation. | ||
+ | |||
+ | Contact: [[team/ |
Prof. Dr. hc. Michael Beetz PhD
Head of Institute
Contact via
Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de
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