We develop a learning-based algorithm for the control of robotic systems governed by unknown, nonlinear dynamics to satisfy tasks expressed as signal temporal logic specifications. Most existing algorithms either assume certain parametric forms for the dynamic terms or resort to unnecessarily large control inputs (e.g., using reciprocal functions) in order to provide theoretical guarantees. The proposed algorithm avoids the aforementioned drawbacks by innovatively integrating neural network-based learning with adaptive control. More specifically, the algorithm learns a controller, represented as a neural network, using training data that correspond to a collection of different tasks and robot parameters. It then incorporates this neural network into an online closed-loop adaptive control mechanism in such a way that the resulting behavior satisfies a user-defined task. The proposed algorithm does not use any information on the unknown dynamic terms or any approximation schemes. We provide formal theoretical guarantees on the satisfaction of the task and we demonstrate the effectiveness of the algorithm in a virtual simulator using a 6-DOF robotic manipulator.
翻译:我们为控制由未知的非线性动态调节的机器人系统开发了一种基于学习的算法,以满足以信号时间逻辑规格表示的任务。大多数现有的算法要么对动态术语采取某些参数形式,要么采用不必要的大型控制投入(例如使用对等功能)来提供理论保证。提议的算法通过创新地整合神经网络学习和适应性控制来避免上述缺陷。更具体地说,算法学习一个作为神经网络代表的控制器,使用与不同任务和机器人参数集合相对应的培训数据。然后将这个神经网络纳入一个在线闭环适应控制机制,使由此产生的行为符合用户定义的任务。提议的算法不使用任何未知动态术语或任何近似计划的任何信息。我们为任务的满意度提供了正式的理论保证,我们用6DF机器人操纵器在虚拟模拟器中展示了算法的有效性。