マルチ抽象化ニューラルコントローラ：対話型運転のための効率的な階層制御アーキテクチャ
Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving
画像入力から離散的な行動計画を生成し、動的運動プリミティブのパラメータを制御する微分可能な階層型制御アーキテクチャを提案。実世界の運転データで行動クローニングを行い、説明性・サンプル効率・人間らしさが向上することを示した。
著者: Xiao Li, Igor Gilitschenski, Guy Rosman, Sertac Karaman, Daniela Rus
分類: cs.RO
原文アブストラクト
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.
