Goal Babbling Permits Direct Learning of Inverse Kinematics

We present an approach to learn inverse kinematics of redundant systems without prior- or expert-knowledge. The method allows for an iterative bootstrapping and refinement of the inverse kinematics estimate. The essential novelty lies in a path-based sampling approach: we generate training data alon...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on autonomous mental development 2010-09, Vol.2 (3), p.216-229
Main Authors: Rolf, M, Steil, J J, Gienger, M
Format: Article
Language:English
Subjects:
Biological system modeling
Degrees of freedom
Estimates
Goal babbling
Humanoid
Humanoid robots
Humans
Inverse kinematics
Inverse problems
Iterative methods
Learning
Manipulators
Mathematical analysis
motor exploration
motor learning
Pediatrics
Predictive models
Robot kinematics
Robots
Sampling
Sampling methods
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present an approach to learn inverse kinematics of redundant systems without prior- or expert-knowledge. The method allows for an iterative bootstrapping and refinement of the inverse kinematics estimate. The essential novelty lies in a path-based sampling approach: we generate training data along paths, which result from execution of the currently learned estimate along a desired path towards a goal. The information structure thereby induced enables an efficient detection and resolution of inconsistent samples solely from directly observable data. We derive and illustrate the exploration and learning process with a low-dimensional kinematic example that provides direct insight into the bootstrapping process. We further show that the method scales for high dimensional problems, such as the Honda humanoid robot or hyperredundant planar arms with up to 50 degrees of freedom.
ISSN:1943-0604
2379-8920
1943-0612
2379-8939
DOI:10.1109/TAMD.2010.2062511