The cerebellum is a neural structure essential for learning, which is connected via multiple zones to many different regions of the brain, and is thought to improve human performance in a large range of sensory, motor and even cognitive processing tasks. An intriguing possibility for the control of complex robotic systems would be to develop an artificial cerebellar chip with multiple zones that could be similarly connected to a variety of subsystems to optimize performance. The novel aim of this paper, therefore, is to propose and investigate a multizone cerebellar chip applied to a range of tasks in robot adaptive control and sensorimotor processing. The multizone cerebellar chip was evaluated using a custom robotic platform consisting of an array of tactile sensors driven by dielectric electroactive polymers mounted upon a standard industrial robot arm. The results demonstrate that the performance in each task was improved by the concurrent, stable learning in each cerebellar zone. This paper, therefore, provides the first empirical demonstration that a synthetic, multizone, cerebellar chip could be embodied within existing robotic systems to improve performance in a diverse range of tasks, much like the cerebellum in a biological system.

中文翻译：

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用于仿生自适应机器人控制和感觉运动处理的多区小脑芯片

小脑是学习必不可少的神经结构，它通过多个区域连接到大脑的许多不同区域，被认为可以提高人类在各种感觉、运动甚至认知处理任务中的表现。控制复杂机器人系统的一个有趣的可能性是开发具有多个区域的人工小脑芯片，这些区域可以类似地连接到各种子系统以优化性能。因此，本文的新目的是提出并研究一种多区小脑芯片，该芯片适用于机器人自适应控制和感觉运动处理中的一系列任务。使用定制的机器人平台评估多区小脑芯片，该平台由安装在标准工业机器人手臂上的介电电活性聚合物驱动的触觉传感器阵列组成。结果表明，每个小脑区的并发、稳定学习都提高了每个任务的性能。因此，本文提供了第一个经验证明，即合成的、多区域的、小脑芯片可以包含在现有的机器人系统中，以提高各种任务的性能，就像生物系统中的小脑一样。