ConfigBot: Adaptive Resource Allocation for Robot Applications in Dynamic Environments

Abstract

The growing use of service robots in dynamic environments requires flexible management of on-board compute resources to optimize the performance of diverse tasks such as navigation, localization, and perception. Current robot deployments often rely on static OS configurations and system over-provisioning. However, they are suboptimal because they do not account for variations in resource usage. This results in poor system-wide behavior such as robot instability or inefficient resource use. This paper presents ConifgBot, a novel system designed to adaptively reconfigure robot applications to meet a predefined performance specification by leveraging \emph{runtime profiling} and \emph{automated configuration tuning}. Through experiments on multiple real robots, each running a different stack with diverse performance requirements, which could be \emph{context}-dependent, we illustrate ConifgBot's efficacy in maintaining system stability and optimizing resource allocation. Our findings highlight the promise of automatic system configuration tuning for robot deployments, including adaptation to dynamic changes.