title: Visual feedback in motion
creator: Smagt, P. van der
creator: Groen, F.
subject: Animal Cognition
subject: Machine Vision
subject: Neural Nets
subject: Robotics
description: In this chapter we introduce a method for model-free monocular visual guidance of a robot arm. The robot arm, with a single camera in its end effector, should be positioned above a stationary target. It is shown that a trajectory can be planned in visual space by using components of the optic flow, and this trajector can be translated to joint torques by a self-learning neural network. No model of the robot, camera, or environment is used. The method reaches a high grasping accuracy after only a few trials.
publisher: Academic Press, Boston, Massachusetts
contributor: Omidvar, O. and Smagt
date: 1997
type: Book Chapter
type: NonPeerReviewed
format: application/postscript
identifier: http://cogprints.org/493/2/SmaGro97.ps
identifier:   Smagt, P. van der and Groen, F.  (1997) Visual feedback in motion.  [Book Chapter]     
relation: http://cogprints.org/493/