Neural Architectures for Robot Intelligence
Neural Architectures for Robot Intelligence
复制标题
机器人智能的神经架构
DOI：
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复制
发表时间：
2004
影响因子：
4.1
通讯作者：
P. McGuire
中科院分区：
文献类型：
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作者：
H. Ritter;Jochen J. Steil;Claudia Nölker;Frank Röthling;P. McGuire
我们认为，通过探索机器人系统的人工控制体系结构的可能性和局限性，可以通过直接实验方法来阐明高等大脑的结构。我们展示了我们最近的一些工作，这些工作受到这一观点的启发，主要围绕着对手部动作各个方面的研究，因为这些方面与许多更高的认知能力密切相关。作为例子，我们报告了一个模块化系统的开发，用于识别基于神经网络的连续手势，使用视觉和触觉感知来指导多指手的可抓握运动，以及识别和使用手势用于机器人教学。关于学习的问题，我们建议从数据挖掘的角度来看待现实世界的学习，并更强烈地关注对观察到的行为的模仿，而不是纯粹的基于强化的探索。作为这种努力的一个具体例子，我们报告了我们实验室正在进行的一个项目的状态，在这个项目中，一个机器人配备了一个具有神经启发结构的注意力系统，通过使用手势和语音命令来教授动作。我们指出了从这个系统中吸取的一些教训，并讨论了这种系统如何有助于研究自然和人工认知系统之间的结合点问题。
We argue that direct experimental approaches to elucidate the architecture of higher brains may benefit from insights gained from exploring the possibilities and limits of artificial control architectures for robot systems. We present some of our recent work that has been motivated by that view and that is centered around the study of various aspects of hand actions since these are intimately linked with many higher cognitive abilities. As examples, we report on the development of a modular system for the recognition of continuous hand postures based on neural nets, the use of vision and tactile sensing for guiding prehensile movements of a multifingered hand, and the recognition and use of hand gestures for robot teaching. Regarding the issue of learning, we propose to view real-world learning from the perspective of data-mining and to focus more strongly on the imitation of observed actions instead of purely reinforcement-based exploration. As a concrete example of such an effort we report on the status of an ongoing project in our laboratory in which a robot equipped with an attention system with a neurally inspired architecture is taught actions by using hand gestures in conjunction with speech commands. We point out some of the lessons learnt from this system, and discuss how systems of this kind can contribute to the study of issues at the junction between natural and artificial cognitive systems.
DOI：
10.1007/3-540-49430-8
发表时间：
2002
期刊：
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影响因子：
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作者：
J. Hartmanis;Takeo Kanade
通讯作者：
J. Hartmanis;Takeo Kanade