Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving
Xiao Li, Igor Gilitschenski, Guy Rosman, Sertac Karaman, Daniela Rus
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 letter, we describe a differentiable and hierarchical control architecture. The proposed representation, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-abstractive neural controller</i> , uses the input image to control the transitions within a novel discrete behavior planner (referred to as the visual automaton generative network, or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vAGN</i> ). 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.
BibTeX
@inproceedings{ral2023_multiabstractive,
title = {Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving},
author = {Xiao Li and Igor Gilitschenski and Guy Rosman and Sertac Karaman and Daniela Rus},
booktitle = {RA-L 2023},
year = {2023}
}