Probabilistic Segmentation Applied to an Assembly Task

被引:0
|
作者
Lioutikov, Rudolf [1 ]
Neumann, Gerhard [1 ]
Maeda, Guilherme [1 ]
Peters, Jan [1 ,2 ]
机构
[1] Tech Univ Darmstadt, Intelligent Autonomous Syst, Darmstadt, Germany
[2] Max Planck Inst Tuebingen, Tubingen, Germany
来源
2015 IEEE-RAS 15TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTS (HUMANOIDS) | 2015年
关键词
PRIMITIVES;
D O I
暂无
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Movement primitives are a well established approach for encoding and executing robot movements. While the primitives themselves have been extensively researched, the concept of movement primitive libraries has not received as much attention. Libraries of movement primitives represent the skill set of an agent and can be queried and sequenced in order to solve specific tasks. The goal of this work is to segment unlabeled demonstrations into an optimal set of skills. Our novel approach segments the demonstrations while learning a probabilistic representation of movement primitives. The 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. Therefore, improving the combined quality of both segmentation and skill learning. Furthermore, our method allows incorporating domain specific insights using heuristics, which are subsequently evaluated and assessed through probabilistic inference methods. We demonstrate our method on a real robot application, where the robot segments demonstrations of a chair assembly task into a skill library. The library is subsequently used to assemble the chair in an order not present in the demonstrations.
引用
收藏
页码:533 / 540
页数:8
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