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teaching:gsoc2017 [2017/02/08 08:12] – [Topic 2: Realistic Grasping using Unreal Engine] lisca | teaching:gsoc2017 [2017/02/08 08:49] – [Topic 3: ROS with PR2 integration in Unreal Engine] lisca | ||
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- | **Main Objective: | + | **Main Objective: |
- | edge detection). | + | able robots in a human environment to recognize objects in diffi- |
+ | cult and challenging scenarios. To achieve this the participant will | ||
+ | develop | ||
+ | object-hypotheses generation and merging. Generating a hypotheses | ||
+ | essentially means to generate regions/ | ||
+ | form a single object or object-part. In particular this entails the de- | ||
+ | velopment of segmentation algorithms for visually challenging scenes | ||
+ | or object properties, as the likes of transparent | ||
+ | occluded scenes. The addressed | ||
+ | objects placed on shelves, objects in drawers, refrigerators, | ||
+ | ers, 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). Specifically we | ||
+ | would start from (but not necessarly limit ourselves to) the implemen- | ||
+ | tation of two state-of-the-art algorithms described in recent papers: | ||
+ | [1] Ilya Lysenkov, Victor Eruhimov, and Gary Bradski, Recognition | ||
+ | and Pose Estimation of Rigid Transparent Objects with a Kinect Sen- | ||
+ | sor, 2013 Robotics: Science and Systems Conference (RSS), 2013. | ||
+ | [2] Richtsfeld A., M ̈ | ||
+ | orwald T., Prankl J., Zillich M. and Vincze | ||
+ | M. - Segmentation of Unknown Objects in Indoor Environments. | ||
+ | IEEE/RSJ International Conference on Intelligent Robots and Sys- | ||
+ | tems (IROS), 2012. | ||
**Task Difficulty: | **Task Difficulty: | ||
| | ||
- | **Requirements: | + | **Requirements: |
+ | edge of CMake. Experience with PCL, OpenCV is prefered. | ||
**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. | **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. | ||
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**Requirements: | **Requirements: | ||
+ | |||
+ | **Expected Results** We expect to be able to load URDF models of | ||
+ | varios robots (e.g. PR2) and be able to control them through ROS | ||
+ | in the game engine. In a similar fashion to a robotic simulator. | ||
+ | |||
+ | Contact: [[team/ | ||
+ | |||
+ | ==== Topic 3: ROS with PR2 integration in Unreal Engine ==== | ||
+ | |||
+ | {{ : | ||
+ | |||
+ | **Main Objective: | ||
+ | PR2 8 robot in [[https:// | ||
+ | |||
+ | **Task Difficulty: | ||
+ | level, as it requires programming skills of various frameworks (ROS, | ||
+ | Linux, Unreal Engine). | ||
+ | | ||
+ | **Requirements: | ||
+ | of the Unreal Engine API, ROS and Linux. Some experience in | ||
+ | robotics. | ||
**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. | **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. |
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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