Spatio-temporal learning with the online finite and infinite echo-state Gaussian processes
File(s)
Accepted version
Author(s)
Soh, H
Demiris, Y
Type
Journal Article
Abstract
Successful biological systems adapt to change. In this paper, we are principally concerned with adaptive systems that operate in environments where data arrives sequentially and is multivariate in nature, for example, sensory streams in robotic systems. We contribute two reservoir inspired methods: 1) the online echostate Gaussian process (OESGP) and 2) its infinite variant, the online infinite echostate Gaussian process (OIESGP) Both algorithms are iterative fixed-budget methods that learn from noisy time series. In particular, the OESGP combines the echo-state network with Bayesian online learning for Gaussian processes. Extending this to infinite reservoirs yields the OIESGP, which uses a novel recursive kernel with automatic relevance determination that enables spatial and temporal feature weighting. When fused with stochastic natural gradient descent, the kernel hyperparameters are iteratively adapted to better model the target system. Furthermore, insights into the underlying system can be gleamed from inspection of the resulting hyperparameters. Experiments on noisy benchmark problems (one-step prediction and system identification) demonstrate that our methods yield high accuracies relative to state-of-the-art methods, and standard kernels with sliding windows, particularly on problems with irrelevant dimensions. In addition, we describe two case studies in robotic learning-by-demonstration involving the Nao humanoid robot and the Assistive Robot Transport for Youngsters (ARTY) smart wheelchair.
Date Issued
2015-03-01
Date Acceptance
2014-03-28
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2015, 26 (3), pp.522-536
ISSN
2162-2388
Publisher
Institute of Electrical and Electronics Engineers
Start Page
522
End Page
536
Journal / Book Title
IEEE Transactions on Neural Networks and Learning Systems
Volume
26
Issue
3
Copyright Statement
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Sponsor
Commission of the European Communities
Identifier
https://ieeexplore.ieee.org/document/6832616
Grant Number
248116
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Hardware & Architecture
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
Gaussian processes (GPs)
machine learning
recurrent neural networks (RNNs)
time-series analysis
Humans
Machine Learning
Neural Networks, Computer
Normal Distribution
Pattern Recognition, Automated
Robotics
Time Factors
Humans
Normal Distribution
Robotics
Time Factors
Pattern Recognition, Automated
Machine Learning
Neural Networks, Computer
Artificial Intelligence & Image Processing
Publication Status
Published
Date Publish Online
2014-06-12