Mimesis from partial observations

被引：0

作者：

Lee, D ^{[1
]}

Nakamura, Y ^{[1
]}

机构：

[1] Univ Tokyo, Dept Mechanoinformat, Bunkyo Ku, Tokyo 1130033, Japan

来源：

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-4 | 2005年

关键词：

imitation; learning; hidden Markov Models;

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：

In this paper, a new mimesis scheme is proposed. This scheme enables for a humanoid to imitate human's motion even though the humanoid cannot see human's whole-body motion and the humanoid has not seen the exactly same motion so far. Mimesis framework is based on continuous Hidden Markov Model. Viterbi algorithm is applied in order to generate more various motion patterns than the number of existing Hidden Markov Models. In order to imitate other's motion in a smooth way, a smoothing technique in generation problem is realized. The feasibility of this method is demonstrated by simulation on a 20 degrees of freedom humanoid robot configuration.

引用

下载

页码：1911 / 1916

页数：6

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