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arXiv · 2305.14797

Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving

Xiao Li·Igor Gilitschenski·Guy Rosman·Sertac Karaman·Daniela Rus
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Abstract

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.

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Xiao Li, Igor Gilitschenski, Guy Rosman, Sertac Karaman, Daniela Rus. 2023-05-24. Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving. https://arxiv.org/abs/2305.14797

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