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The user needs to provide a method for grasp generation based on the real visual input. The grasps are then planned, executed, and evaluated by the provided grasp simulator where several grasp-quality measures are used for evaluation. This setup has the advantage that a large number of grasps can be executed and evaluated while dealing with dynamics and the noise and uncertainty present in the real world images. VisGraB enables a fair comparison among different grasping methods. The user furthermore does not need to deal with robot hardware, focusing on the vision methods instead. As a baseline, benchmark results of our grasp strategy are included.<\/jats:p>","DOI":"10.2478\/s13230-012-0020-5","type":"journal-article","created":{"date-parts":[[2012,5,17]],"date-time":"2012-05-17T17:07:45Z","timestamp":1337274465000},"source":"Crossref","is-referenced-by-count":20,"title":["VisGraB: A Benchmark for Vision-Based Grasping"],"prefix":"10.2478","volume":"3","author":[{"given":"Gert","family":"Kootstra","sequence":"first","affiliation":[]},{"given":"Mila","family":"Popovi\u0107","sequence":"additional","affiliation":[]},{"given":"Jimmy Alison","family":"J\u00f8rgensen","sequence":"additional","affiliation":[]},{"given":"Danica","family":"Kragic","sequence":"additional","affiliation":[]},{"given":"Henrik Gordon","family":"Petersen","sequence":"additional","affiliation":[]},{"given":"Norbert","family":"Kr\u00fcger","sequence":"additional","affiliation":[]}],"member":"374","reference":[{"issue":"3","key":"20_CR1","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/TRO.2011.2132870","volume":"27","author":"Y. 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