Learning an internal representation of the end-effector configuration space
Learning an internal representation of the end-effector configuration space
著者: Alban Laflaquière, Alexander V. Terekhov, Bruno Gas, J. Kevin O'Regan
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors 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.