Cart (Loading....) | Create Account
Close category search window
 

Neural learning and dynamical selection of redundant solutions for inverse kinematic control

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Reinhart, R.F. ; Res. Inst. for Cognition & Robot. (CoR-Lab.), Bielefeld Univ., Bielefeld, Germany ; Steil, J.J.

We introduce a novel recurrent neural network controller that learns and maintains multiple solutions of the inverse kinematics. Redundancies are resolved dynamically by means of multi-stable attractor dynamics. The associative net- work comprises a combined forward and inverse model of the robot's kinematics and enables flexible selection of control spaces by mixing constraints in task space and joint space. The network is integrated into a feedforward-feedback control framework which enables dynamical movement generation. We show results for the humanoid robot iCub in simulation.

Published in:

Humanoid Robots (Humanoids), 2011 11th IEEE-RAS International Conference on

Date of Conference:

26-28 Oct. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.