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**When contacting us, please make sure you read the description of the topic you are interested in carefully. Only contact the person responsible for the topic / topics you are interested in. Please only ask topic-relevant specific questions, otherwise your emails will not be answered due to limited resources we have for processing the vast amount of GSoC inquiries. For more general questions please use our [[https:// | **When contacting us, please make sure you read the description of the topic you are interested in carefully. Only contact the person responsible for the topic / topics you are interested in. Please only ask topic-relevant specific questions, otherwise your emails will not be answered due to limited resources we have for processing the vast amount of GSoC inquiries. For more general questions please use our [[https:// | ||
+ | |||
+ | ===== KnowRob -- Robot Knowledge Processing ===== | ||
+ | |||
+ | KnowRob is a knowledge processing system that combines knowledge representation | ||
+ | and reasoning methods with techniques for acquiring knowledge from different | ||
+ | sources and for grounding the knowledge in a physical system. It provides | ||
+ | robots with knowledge to be used in their tasks, for example action | ||
+ | descriptions, | ||
+ | hardware and capabilities. The knowledge base is complemented with reasoning | ||
+ | methods and techniques for grounding abstract, high-level information about | ||
+ | actions and objects in the perceived sensor data. | ||
+ | |||
+ | KnowRob became the main knowledge base in the ROS ecosystem and is actively | ||
+ | being used in different academic and industrial research labs around the | ||
+ | world. Several European research projects use the system for a wide range | ||
+ | of applications, | ||
+ | describing multi-robot search-and-rescue tasks ([[www.sherpa-project.eu|SHERPA]]), | ||
+ | people in their homes (SRS) to industrial assembly tasks ([[http:// | ||
+ | |||
+ | KnowRob is an open-source project hosted at [[http:// | ||
+ | that also provides extensive documentation on its [[http:// | ||
+ | |||
+ | ===== CRAM -- Robot Plans ===== | ||
+ | |||
+ | CRAM is a high-level system for designing and performing abstract | ||
+ | robot plans to define intelligent robot behavior. It consists of a | ||
+ | library of generic, robot platform independent plans, elaborate | ||
+ | reasoning mechanisms for detecting and repairing plan failures, as | ||
+ | well as interface modules for executing these plans on real robot | ||
+ | hardware. It supplies robots with concurrent, reactive task execution | ||
+ | capabilities and makes use of knowledge processing backends, such as | ||
+ | KnowRob, for information retrieval. | ||
+ | |||
+ | CRAM builds on top of the ROS ecosystem and is actively developed as an | ||
+ | [[http:// | ||
+ | It is the basis for high-level robot control in many parts of the | ||
+ | world, especially in several European research projects covering | ||
+ | applications from geometrically abstract object manipulation | ||
+ | (RoboHow), multi-robot task coordination and execution (SHERPA), | ||
+ | experience based task parametrization retrieval (RoboEarth), | ||
+ | human robot interaction (SAPHARI). | ||
+ | Further information, | ||
+ | use-cases can be found at the [[http:// | ||
+ | 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, | ||
+ | |||
+ | The OpenEASE web interface as well as further information and publication material can be accessed through its publicly available [[http:// | ||
+ | |||
+ | ===== 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/ | ||
+ | |||
+ | ===== 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 ==== | ||
+ | |||
+ | {{ : | ||
+ | |||
+ | **Main Objective: | ||
+ | edge detection). | ||
+ | |||
+ | **Task Difficulty: | ||
+ | | ||
+ | **Requirements: | ||
+ | |||
+ | **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/ | ||
+ | |||
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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