creators_name: Smagt, P. van der
creators_name: Groen, F.
editors_name: Omidvar, O. and Smagt
type: bookchapter
datestamp: 1998-07-03
lastmod: 2011-03-11 08:54:00
metadata_visibility: show
title: Visual feedback in motion
ispublished: pub
subjects: bio-ani-cog
subjects: comp-sci-mach-vis
subjects: comp-sci-neural-nets
subjects: comp-sci-robot
full_text_status: public
abstract: 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.
date: 1997
date_type: published
publication: Neural Systems for Robotics
publisher: Academic Press, Boston, Massachusetts
pagerange: 37-73
refereed: FALSE
citation:   Smagt, P. van der and Groen, F.  (1997) Visual feedback in motion.  [Book Chapter]     
document_url: http://cogprints.org/493/2/SmaGro97.ps