Learning to Walk Using a Recurrent Neural Network with Time Delay

被引：0

作者：

Ammar, Boudour ^{[1
]}

Chouikhi, Naima ^{[1
]}

Alimi, Adel M. ^{[1
]}

Cherif, Farouk ^{[2
]}

Rezzoug, Nasser ^{[3
]}

Gorce, Philippe ^{[3
]}

机构：

[1] Univ Sfax, Natl Engn Sch Sfax ENIS, REGIM REs Grp Intelligent Machines, BP 1173, Sfax 3038, Tunisia

[2] Ecole Super Sci & Technol, Spcials Funct & Applicat, ISSATS Lab Math Phys, LR11ES35, Sousse 4002, Tunisia

[3] Univ South Toulon, HandiBio Lab, F-83597 La Garde, France

来源：

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2013 | 2013年 / 8131卷

关键词：

Human walking; 3D human simulator; Recurrent Neural Network; Biomechanics; e-kalman filter;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Walking based on gaits is one of the most approved methodologies for walking robot. In this paper, we develop learning strategy for walking biped robot or human based on a self made database using biomechanical capture. This system is provided by a Recurrent Neural Network (RNN) with an internal discrete time delay. The role of the proposed network is the training of human walking data by giving an estimation of the biped's next position at each time and achieve a human-like natural walking. Different architectures of RNN are proposed and tested. In Particular, a comparative study is given and the results of the RNN mixed with extended Kalman filter are illustrated.

引用

页码：511 / 518

页数：8

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