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JACIII Vol.22 No.6 pp. 965-977
doi: 10.20965/jaciii.2018.p0965
(2018)

Paper:

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Humanoid Robot Motion Modeling Based on Time-Series Data Using Kernel PCA and Gaussian Process Dynamical Models

Jian Mi and Yasutake Takahashi

Department of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui
3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan

Received:
February 9, 2018
Accepted:
July 30, 2018
Published:
October 20, 2018
Creative Commons License
Keywords:
humanoid robot, dimensionality reduction, kernel PCA, humanoid motion models, Gaussian process dynamical models
Abstract

In this article, contrary to popular studies on human motion learning, we focus on addressing the problem of humanoid robot motions directly. Performances of different kernel functions with principal components analysis (PCA) in Gaussian process dynamical models (GPDM) are investigated to build efficient humanoid robot motion models. A novel kernel-PCA-GPDM method is proposed for building different types of humanoid robot motion models. Compared with the standard-PCA-GPDM and auto-encoder-GPDM methods, our proposed method is more efficient in humanoid robot motion modeling. In this work, three types of NAO robot motion models are studied: walk-model, lateral-walk model, and wave-hand model, where motion data are collected from an Aldebaran NAO robot using magnetic rotary encoder sensors. Using kernel-PCA-GPDM method, the motion data are first projected from the high 23-dimension observation space to a 3-dimension low latent space. Then, three types of humanoid robot motion models are learned in the 3D latent space. Compared with other kernel-PCA-GPDM or auto-encoder-GPDM methods, our proposed novel kernel-PCA-GPDM method performs efficiently in motion learning. Finally, we realize humanoid robot motion representation to verify the motion models that we build. The experimental results show that our proposed kernel-PCA-GPDM method builds efficient and smooth motion models.

Three types of humanoid robot motion models are studied. Using kernel-PCA GPDM

Three types of humanoid robot motion models are studied. Using kernel-PCA GPDM"," 3D motion models are built from 23-dimensional humanoid robot motion data. Then"," the learned 3D motion data are restored to verify the learned motion models

Cite this article as:
J. Mi and Y. Takahashi, “Humanoid Robot Motion Modeling Based on Time-Series Data Using Kernel PCA and Gaussian Process Dynamical Models,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.6, pp. 965-977, 2018.
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