Robot Navigation and Manipulation based on a Predictive Associative Memory

被引：0

作者：

Jockel, Sascha ^{[1
]}

Mendes, Mateus ^{[2
]}

Zhang, Jianwei ^{[1
]}

Coimbra, A. Paulo ^{[3
]}

Crisostomo, Manuel ^{[3
]}

机构：

[1] Univ Hamburg, CINACS Int Res Training Grp, Dept Informat, Martinistr 52, Hamburg, Germany

[2] IPC, ESTGOH, Coimbra, Portugal

[3] Univ Coimbra, ISR, Dept Elect & Comp Engn, Coimbra, Portugal

来源：

2009 IEEE 8TH INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING | 2009年

关键词：

Sparse distributed memory (SDM); EPIROME; episodic memory; associative memory; navigation; manipulation; robotics;

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：

Proposed in the 1980s, the Sparse Distributed Memory (SDM) is a model of an associative memory based on the properties of a high dimensional binary space. This model has received some attention from researchers of different areas and has been improved over time. However, a few problems have to be solved when using it in practice, due to the non-randomness characteristics of the actual data. We tested an SDM using different forms of encoding the information, and in two different domains: robot navigation and manipulation. Our results show that the performance of the SDM in the two domains is affected by the way the information is actually encoded, and may be improved by some small changes in the model.

引用

页码：85 / +

页数：2

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