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Searching a scalable approach to cerebellar based control

机译:寻找一种可扩展的方法来实现基于小脑的控制

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摘要

Decades of research into the structure and function of the cerebellum have led to a clear understanding of many of its cells, as well as how learning might take place. Furthermore, there are many theories on what signals the cerebellum operates on, and how it works in concert with other parts of the nervous system. Nevertheless, the application of computational cerebellar models to the control of robot dynamics remains in its infant state. To date, few applications have been realized. The currently emerging family of light-weight robots (Hirzinger, in Proc. Second ecpd Int. Conference on Advanced Robotics, Intelligent Automation and Active Systems, 1996) poses a new challenge to robot control: due to their complex dynamics traditional methods, depending on a full analysis of the dynamics of the system, are no longer applicable since the joints influence each other dynamics during movement. Can artificial cerebellar models compete here? [References: 78]
机译:对小脑的结构和功能进行了数十年的研究,使人们对小脑的许多细胞以及如何进行学习有了清晰的认识。此外,关于小脑的信号作用及其与神经系统其他部分协同作用的理论很多。尽管如此,小脑计算模型在机器人动力学控制中的应用仍处于婴儿期。迄今为止,几乎没有实现任何应用。当前新兴的轻型机器人家族(Hirzinger,在第二届ecpd国际高级机器人,智能自动化和主动系统会议上,1996年)对机器人控制提出了新的挑战:由于它们复杂的动力学,传统方法取决于由于关节在运动过程中会相互影响动力学,因此不再需要对系统动力学进行全面分析。人工小脑模型可以在这里竞争吗? [参考:78]

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