Publications
Detailed Information
Gaussian Model-Based Methods for Task Constraint Learning and Optimal Motion Generation of High-Dimensional Robot Systems
Cited 0 time in
Web of Science
Cited 0 time in Scopus
- Authors
- Advisor
- 박종우
- Major
- 공과대학 기계항공공학부
- Issue Date
- 2015-02
- Publisher
- 서울대학교 대학원
- Description
- 학위논문 (박사)-- 서울대학교 대학원 : 기계항공공학부, 2015. 2. 박종우.
- Abstract
- This thesis is concerned with motion learning for complex, high-dimensional robot systems operating in unstructured environments, and subject to various task constraints whose analytical characterization may not be a priori available. We first present a Gaussian process algorithm for learning the configuration space of a robot
subject to holonomic task constraints. Given an observed data set of points that lie on this task constrained configuration space, or constraint manifold, a point-to-
manifold distance function is constructed that measures the distance of any given point from the constraint manifold. The observed data are first encoded using a
Gaussian mixture model, and the distance function is learned via Gaussian process regression. The constructed distance function admits an explicit representation that
can be differentiated to obtain analytic gradients. We apply this distance function and its gradient to a sampling-based path planning problem for a robot performing a constrained task. We also propose an efficient method for generating suboptimal motions for multibody systems using Gaussian process dynamical models. Given a dynamical model for a multibody system, and a trial motion, a lower-dimensional Gaussian process
dynamical model is fitted to the trial motion. New motions are then generated by performing a dynamic optimization in the lower-dimensional space. We introduce
the notion of variance tubes as an intuitive and efficient means of restricting the optimization search space. The performance of our algorithm is evaluated through detailed case studies of raising motions for an arm, swing, pitching and jumping motions for a humanoid and lifting motions for a mobile manipulator.
- Language
- English
- Files in This Item:
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.