Emergent synthesis of motion patterns for locomotion robots

M.M. Svinin , K. Yamada , K. Ueda
{"title":"Emergent synthesis of motion patterns for locomotion robots","authors":"M.M. Svinin ,&nbsp;K. Yamada ,&nbsp;K. Ueda","doi":"10.1016/S0954-1810(01)00027-9","DOIUrl":null,"url":null,"abstract":"<div><p>Emergence of stable gaits in locomotion robots is studied in this paper. A classifier system, implementing an instance-based reinforcement-learning scheme, is used for the sensory-motor control of an eight-legged mobile robot and for the synthesis of the robot gaits. The robot does not have a priori knowledge of the environment and its own internal model. It is only assumed that the robot can acquire stable gaits by learning how to reach a goal area. During the learning process the control system is self-organized by reinforcement signals. Reaching the goal area defines a global reward. Forward motion gets a local reward, while stepping back and falling down get a local punishment. As learning progresses, the number of the action rules in the classifier systems is stabilized to a certain level, corresponding to the acquired gait patterns. Feasibility of the proposed self-organized system is tested under simulation and experiment. A minimal simulation model that does not require sophisticated computational schemes is constructed and used in simulations. The simulation data, evolved on the minimal model of the robot, is downloaded to the control system of the real robot. Overall, of 10 simulation data seven are successful in running the real robot.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2001-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(01)00027-9","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0954181001000279","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22

Abstract

Emergence of stable gaits in locomotion robots is studied in this paper. A classifier system, implementing an instance-based reinforcement-learning scheme, is used for the sensory-motor control of an eight-legged mobile robot and for the synthesis of the robot gaits. The robot does not have a priori knowledge of the environment and its own internal model. It is only assumed that the robot can acquire stable gaits by learning how to reach a goal area. During the learning process the control system is self-organized by reinforcement signals. Reaching the goal area defines a global reward. Forward motion gets a local reward, while stepping back and falling down get a local punishment. As learning progresses, the number of the action rules in the classifier systems is stabilized to a certain level, corresponding to the acquired gait patterns. Feasibility of the proposed self-organized system is tested under simulation and experiment. A minimal simulation model that does not require sophisticated computational schemes is constructed and used in simulations. The simulation data, evolved on the minimal model of the robot, is downloaded to the control system of the real robot. Overall, of 10 simulation data seven are successful in running the real robot.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
运动机器人运动模式的紧急综合
研究了运动机器人稳定步态的产生问题。基于实例强化学习的分类器系统用于八足移动机器人的感觉运动控制和机器人步态的综合。机器人没有对环境和自身内部模型的先验知识。仅假设机器人通过学习如何到达目标区域而获得稳定的步态。在学习过程中,控制系统通过强化信号进行自组织。达到目标区域定义了一个全局奖励。向前移动得到局部奖励,而后退和摔倒得到局部惩罚。随着学习的进行,分类器系统中动作规则的数量稳定在一定的水平,与习得的步态模式相对应。仿真和实验验证了该自组织系统的可行性。构建了一个不需要复杂计算方案的最小仿真模型并用于仿真。在机器人的最小模型上演化出的仿真数据被下载到真实机器人的控制系统中。总的来说,10个模拟数据中有7个在实际机器人运行中是成功的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Volume Contents Simulating behaviors of human situation awareness under high workloads Emergent synthesis of motion patterns for locomotion robots Synthesis and emergence — research overview Concept of self-reconfigurable modular robotic system
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1