Online Learning for Behavior Switching in a Soft Robotic Arm

被引：0
|
作者：
Li, Tao [1 ]
Nakajima, Kohei [1 ]
Pfeifer, Rolf [1 ]
机构：
[1] Univ Zurich, Dept Informat, Artificial Intelligence Lab, CH-8050 Zurich, Switzerland
来源：
2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) | 2013年
关键词：
PATTERNS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Soft robots possess several potential advantages over traditional articulated ones and have attracted significant interest in recent years. However, to control this new type of robots using conventional model-based robotic control approaches is generally ineffective. In this paper, we investigate the challenge to embed and switch among multiple behaviors for an octopus-inspired soft robotic arm. An online learning method for reservoir computing is exploited for this task. This online learning method does not require a separate teaching data collection phase; thus, it has the potential to achieve autonomy in soft robots. Our result shows the feasibility of this approach.
引用
收藏
页码：1296 / 1302
页数：7
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