Ritter H, Steil J J, Nölker C, Röthling F, McGuire P
Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany.
Rev Neurosci. 2003;14(1-2):121-43. doi: 10.1515/revneuro.2003.14.1-2.121.
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.
我们认为，阐明高等大脑结构的直接实验方法可能会受益于探索机器人系统人工控制架构的可能性和局限性所获得的见解。我们展示了一些近期受该观点启发且围绕手部动作各方面研究展开的工作，因为这些与许多高等认知能力密切相关。例如，我们报告了基于神经网络的连续手部姿势识别模块化系统的开发、利用视觉和触觉传感来引导多指手的抓握动作，以及机器人教学中手势的识别与应用。关于学习问题，我们建议从数据挖掘的角度看待现实世界的学习，并更加强调对观察到的动作进行模仿，而非单纯基于强化的探索。作为此类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进展情况，在该项目中，一个配备具有神经启发架构注意力系统的机器人通过结合语音命令使用手势来学习动作。我们指出了从这个系统中学到的一些经验教训，并讨论了这类系统如何能够为自然认知系统与人工认知系统交叉领域问题的研究做出贡献。
