As learning-based methods make their way from perception systems to planning/control stacks, robot control systems have started to enjoy the benefits that data-driven methods provide. Because control systems directly affect the motion of the robot, data-driven methods, especially black box approaches, need to be used with caution considering aspects such as stability and interpretability. In this paper, we describe a differentiable and hierarchical control architecture. The proposed representation, called \textit{multi-abstractive neural controller}, uses the input image to control the transitions within a novel discrete behavior planner (referred to as the visual automaton generative network, or \textit{vAGN}). The output of a vAGN controls the parameters of a set of dynamic movement primitives which provides the system controls. We train this neural controller with real-world driving data via behavior cloning and show improved explainability, sample efficiency, and similarity to human driving.

翻译：随着基于学习方法从感知系统逐步扩展至规划与控制栈，机器人控制系统开始受益于数据驱动方法。由于控制系统直接影响机器人运动，数据驱动方法，特别是黑箱方法，在稳定性和可解释性等方面需谨慎使用。本文描述了一种可微分层控制架构。所提出的表示方法称为多抽象神经控制器，它利用输入图像控制新型离散行为规划器（称为视觉自动机生成网络，简称vAGN）的状态转移。vAGN的输出控制一组动态运动基元的参数，从而提供系统控制信号。我们通过行为克隆使用真实驾驶数据训练该神经控制器，并展示了其改进的可解释性、样本效率以及与人类驾驶的相似性。