The presence of computation and transmission-variable time delays within a robotic control loop is a major cause of instability, hindering safe human-robot interaction (HRI) under these circumstances. Classical control theory has been adapted to counteract the presence of such variable delays; however, the solutions provided to date cannot cope with HRI robotics inherent features. The highly nonlinear dynamics of HRI cobots (robots intended for human interaction in collaborative tasks), together with the growing use of flexible joints and elastic materials providing passive compliance, prevent traditional control solutions from being applied. Conversely, human motor control natively deals with low power actuators, nonlinear dynamics, and variable transmission time delays. The cerebellum, pivotal to human motor control, is able to predict motor commands by correlating current and past sensorimotor signals, and to ultimately compensate for the existing sensorimotor human delay (tens of milliseconds). This work aims at bridging those inherent features of cerebellar motor control and current robotic challenges—namely, compliant control in the presence of variable sensorimotor delays. We implement a cerebellar-like spiking neural network (SNN) controller that is adaptive, compliant, and robust to variable sensorimotor delays by replicating the cerebellar mechanisms that embrace the presence of biological delays and allow motor learning and adaptation.

中文翻译：

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机器人控制中非确定性时间延迟问题的基于小脑的解决方案

机器人控制回路中存在计算和传输可变时间延迟是不稳定的主要原因，在这些情况下阻碍了安全的人机交互 (HRI)。经典控制理论已被用于抵消这种可变延迟的存在；然而，迄今为止提供的解决方案无法应对 HRI 机器人技术的固有特性。HRI 协作机器人（用于协作任务中的人机交互的机器人）的高度非线性动力学，以及提供被动顺应性的柔性关节和弹性材料的日益使用，阻碍了传统控制解决方案的应用。相反，人体运动控制本身就可以处理低功率执行器、非线性动力学和可变传输时间延迟。小脑，对人体运动控制至关重要，能够通过关联当前和过去的感觉运动信号来预测运动命令，并最终补偿现有的感觉运动人类延迟（数十毫秒）。这项工作旨在弥合小脑运动控制的固有特征和当前的机器人挑战——即在存在可变感觉运动延迟的情况下进行顺从控制。我们实现了一个类似小脑的尖峰神经网络 (SNN) 控制器，该控制器通过复制包含生物延迟并允许运动学习和适应的小脑机制，对可变感觉运动延迟具有自适应、顺应性和鲁棒性。这项工作旨在弥合小脑运动控制的固有特征和当前的机器人挑战——即在存在可变感觉运动延迟的情况下进行顺从控制。我们实现了一个类似小脑的尖峰神经网络 (SNN) 控制器，该控制器通过复制包含生物延迟并允许运动学习和适应的小脑机制，对可变感觉运动延迟具有自适应、顺应性和鲁棒性。这项工作旨在弥合小脑运动控制的固有特征和当前的机器人挑战——即在存在可变感觉运动延迟的情况下进行顺从控制。我们实现了一个类似小脑的尖峰神经网络 (SNN) 控制器，该控制器通过复制包含生物延迟并允许运动学习和适应的小脑机制，对可变感觉运动延迟具有自适应、顺应性和鲁棒性。