Metareasoning for Safe Decision Making in Autonomous Systems
Justin Svegliato, Connor Basich, Sandhya Saisubramanian, Shlomo Zilberstein
Abstract
Although experts carefully specify the high-level decision-making models in autonomous systems, it is infeasible to guarantee safety across every scenario during operation. We therefore propose a safety metareasoning system that optimizes the severity of the system's safety concerns and the interference to the system's task: the system executes in parallel a task process that completes a specified task and safety processes that each address a specified safety concern with a conflict resolver for arbitration. This paper offers a formal definition of a safety metareasoning system, a recommendation algorithm for a safety process, an arbitration algorithm for a conflict resolver, an application of our approach to planetary rover exploration, and a demonstration that our approach is effective in simulation.
BibTeX
@inproceedings{icra2022_metareasoningfor,
title = {Metareasoning for Safe Decision Making in Autonomous Systems},
author = {Justin Svegliato and Connor Basich and Sandhya Saisubramanian and Shlomo Zilberstein},
booktitle = {ICRA 2022},
year = {2022}
}