Learning inverse kinematics

Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Real-time control of the end-effector of a humanoid robot in external coordinates requires computationally efficient solutions of the inverse kinematics problem. In this context, this paper investigates inverse kinematics learning for resolved motion rate control (RMRC) employing an optimization criterion to resolve kinematic redundancies. Our learning approach is based on the key observations that learning an inverse of a nonuniquely invertible function can be accomplished by augmenting the input representation to the inverse model and by using a spatially localized learning approach. We apply this strategy to inverse kinematics learning and demonstrate how a recently developed statistical learning algorithm, locally weighted projection regression, allows efficient learning of inverse kinematic mappings in an incremental fashion even when input spaces become rather high dimensional. Our results are illustrated with a 30-DOF humanoid robot.
Original languageEnglish
Title of host publicationProceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium
Pages298-303
Number of pages6
Volume1
DOIs
Publication statusPublished - 29 Oct 2001
Event2001 IEEE/RSJ International Confernece on Intelligent Robots and Systems - Maui, United States
Duration: 29 Oct 20013 Nov 2001

Conference

Conference2001 IEEE/RSJ International Confernece on Intelligent Robots and Systems
Abbreviated titleIROS 2001
CountryUnited States
CityMaui
Period29/10/013/11/01

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