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ロボカップを用いたマルチロボット環境における学習・発達・進化手法の共同開発

使用 RoboCup 联合开发多机器人环境中的学习、开发和进化方法

基本信息

批准号：
11694155
负责人：
ASADA Minoru
金额：
$ 5.31万
依托单位：
Osaka University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1999
资助国家：
日本
起止时间：
1999 至 2001
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-11694155/
关键词：
RoboCup multi-robot learning development Evolutionary Approach embodiment

项目摘要

RoboCup proposed as an international joint research project aims at building a new paradigm of AI and robotics where broad range of research issues related to the multi-robot environment have been attacked. In our project, we have focused on learning, development, and evolutionary approaches and verified their validity through the real robot experiments. In the Osaka University group, learning and evolutionary approaches have been adopted in order to realize the basic skills and cooperative behaviors. Especially, the adaptive fitness function worked well for developmental skill improvement which has been demonstrated in the RoboCup competitions. In the Southern California University group, ***phologically reconfigure robots have been proposed and implemented. Cooperative behaviors between such modular robots were controlled by a concept of hormone. In the Zurich University group, a fundamental study on concept acquisition of object from sensori-motor map tang was conducted. Further, a basic architecture for concept learning from row sensory information was proposed based on the information theoretic constraint. These research achievements were presented in the RoboCup workshops or symposiums, and we had very constructs discussion towards the next research projects. Note that the main purpose of the project was to support the travel expenses and the individual research projects have their own research funds.

RoboCup是一个国际联合研究项目，旨在建立一个新的人工智能和机器人范式，其中与多机器人环境相关的广泛研究问题已经受到攻击。在我们的项目中，我们专注于学习，发展和进化的方法，并通过真实的机器人实验验证其有效性。在大坂大学组中，学习和进化的方法已被采用，以实现基本技能和合作行为。特别是，自适应功能工作良好的发展技能的提高，已被证明在RoboCup比赛。在南加州大学的研究小组中，已经提出并实现了 *phologically重新配置的机器人。这种模块化机器人之间的合作行为是由激素的概念控制的。在苏黎世大学的小组中，进行了一项关于从感觉-运动地图中获得物体概念的基础研究。在此基础上，提出了基于信息论约束的行感知信息概念学习的基本框架。这些研究成果在RoboCup的研讨会上进行了展示，并对下一步的研究项目进行了深入的讨论。请注意，该项目的主要目的是支持差旅费和个人研究项目有自己的研究基金。