Learning an internal representation of the end-effector configuration space

被引：0
|
作者：
Laflaquiere, Alban [1 ,2 ]
Terekhov, Alexander V. [1 ,2 ]
Gas, Bruno [1 ,2 ]
O'Regan, J. Kevin [3 ,4 ]
机构：
[1] Univ Paris 06, F-75005 Paris, France
[2] ISIR, CNRS, UMR 7222, F-75005 Paris, France
[3] Univ Paris 05 Descartes, F-75005 Paris, France
[4] LPP, CNRS, UMR 8158, F-75005 Paris, France
来源：
2013 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) | 2013年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Current machine learning techniques proposed to automatically discover a robot's kinematics usually rely on a priori information about the robot's structure, sensor properties or end-effector position. This paper proposes a method to estimate a certain aspect of the forward kinematics model with no such information. An internal representation of the end-effector configuration is generated from unstructured proprioceptive and exteroceptive data flow under very limited assumptions. A mapping from the proprioceptive space to this representational space can then be used to control the robot.
引用
收藏
页码：1230 / 1235
页数：6
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