{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T08:57:02Z","timestamp":1649149022336},"reference-count":19,"publisher":"Walter de Gruyter GmbH","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,1,30]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Action affordance learning based on visual sensory information is a crucial problem within the development of cognitive agents. In this paper, we present a method for learning action affordances based on basic visual features, which can vary in their granularity, order of combination and semantic content. The method is provided with a large and structured set of visual features, motivated by the visual hierarchy in primates and finds relevant feature action associations automatically. We apply our method in a simulated environment on three different object sets for the case of grasp affordance learning. For box objects,we achieve a 0.90 success probability, 0.80 for round objects and up to 0.75 for open objects, when presented with novel objects. In thiswork,we demonstrate, in particular, the effect of choosing appropriate feature representations. We demonstrate a significant performance improvement by increasing the complexity of the perceptual representation. By that, we present important insights in how the design of the feature space influences the actual learning problem.<\/jats:p>","DOI":"10.1515\/pjbr-2015-0006","type":"journal-article","created":{"date-parts":[[2015,4,20]],"date-time":"2015-04-20T13:45:51Z","timestamp":1429537551000},"source":"Crossref","is-referenced-by-count":0,"title":["Identifying relevant feature-action associations for grasping unmodelled objects"],"prefix":"10.1515","volume":"6","author":[{"given":"Mikkel Tang","family":"Thomsen","sequence":"first","affiliation":[]},{"given":"Dirk","family":"Kraft","sequence":"additional","affiliation":[]},{"given":"Norbert","family":"Kr\u00fcger","sequence":"additional","affiliation":[]}],"member":"374","reference":[{"key":"ref191","first-page":"2019","article-title":"Probabilistic sensorbased grasping in Intelligent Robots and Systems RSJ International Conference on pp","author":"Laaksonen","year":"2012","journal-title":"IEEE IEEE"},{"key":"ref81","doi-asserted-by":"publisher","first-page":"5008","DOI":"10.1007\/978-3-540-79547-6_42","article-title":"Functional object class detection based on learned affordance cues in Computer Vision Systems ser Lecture Notes in Computer Springer Berlin Heidelberg vol pp Available http dx doi org","author":"Stark","year":"2008","journal-title":"Science Online"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1177\/0278364912452621","article-title":"Enabling grasping of unknown objects through a synergistic use of edge and surface information The of vol no pp Available http ijr sagepub com content abstract","volume":"2","author":"Kootstra","year":"2012","journal-title":"International Journal Robotics Research Online"},{"key":"ref291","first-page":"1","article-title":"Robwork : A flexible toolbox for robotics research and education Robotics st International Symposium on and th German Conference on Robotics pp june","volume":"6","author":"Ellekilde","year":"2010","journal-title":"ROBOTIK"},{"key":"ref91","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/11840541_5","article-title":"Visual learning of affordance based cues in Proceedings of the th International Conference on From Animals to Animats : Simulation of Adaptive Behavior ser SAB Heidelberg : Springer - 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