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Libraries of movement primitives represent the skill set of an agent. Primitives can be queried and sequenced in order to solve specific tasks. The goal of this work is to segment unlabeled demonstrations into a representative set of primitives. Our proposed method differs from current approaches by taking advantage of the often neglected, mutual dependencies between the segments contained in the demonstrations and the primitives to be encoded. By exploiting this mutual dependency, we show that we can improve both the segmentation and the movement primitive library. Based on probabilistic inference our novel approach segments the demonstrations while learning a probabilistic representation of movement primitives. We demonstrate our method on two real robot applications. First, the robot segments sequences of different letters into a library, explaining the observed trajectories. Second, the robot segments demonstrations of a chair assembly task into a movement primitive library. The library is subsequently used to assemble the chair in an order not present in the demonstrations.<\/jats:p>","DOI":"10.1177\/0278364917713116","type":"journal-article","created":{"date-parts":[[2017,7,21]],"date-time":"2017-07-21T07:50:40Z","timestamp":1500623440000},"page":"879-894","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":54,"title":["Learning movement primitive libraries through probabilistic segmentation"],"prefix":"10.1177","volume":"36","author":[{"given":"Rudolf","family":"Lioutikov","sequence":"first","affiliation":[{"name":"Intelligent Autonomous Systems, TU Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerhard","family":"Neumann","sequence":"additional","affiliation":[{"name":"Computational Learning for Autonomous Systems, Robotics & Autonomous Systems, University of Lincoln, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guilherme","family":"Maeda","sequence":"additional","affiliation":[{"name":"Intelligent Autonomous Systems, TU Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Peters","sequence":"additional","affiliation":[{"name":"Intelligent Autonomous Systems, TU Darmstadt, Germany"},{"name":"Max Planck Institute Tuebingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,7,21]]},"reference":[{"key":"bibr1-0278364917713116","first-page":"185","volume-title":"Proceedings of the 2004 graphics interface conference","author":"Barbi\u010d J","year":"2004"},{"key":"bibr2-0278364917713116","volume-title":"Pattern Recognition and Machine Learning","volume":"1","author":"Bishop CM","year":"2006"},{"key":"bibr3-0278364917713116","doi-asserted-by":"publisher","DOI":"10.1109\/34.868685"},{"key":"bibr4-0278364917713116","first-page":"388","volume-title":"Advances in neural information processing systems 23: 24th Annual conference on neural information processing systems 2010. proceedings of a meeting held Vancouver","author":"Chiappa S","year":"2010"},{"key":"bibr5-0278364917713116","first-page":"141","volume-title":"Advances in neural information processing systems 14, neural information processing systems: Natural and synthetic, (NIPS) 2001","volume":"1","author":"d\u2019Avella A","year":"2002"},{"key":"bibr6-0278364917713116","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2013.00068"},{"key":"bibr7-0278364917713116","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013254724861"},{"key":"bibr8-0278364917713116","unstructured":"Fox E (2009) Bayesian nonparametric learning of complex dynamical phenomena. 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