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.
View on arXiv@article{dwivedula2025_2501.10513,
title={ ConfigBot: Adaptive Resource Allocation for Robot Applications in Dynamic Environments },
author={ Rohit Dwivedula and Sadanand Modak and Aditya Akella and Joydeep Biswas and Daehyeok Kim and Christopher J. Rossbach },
journal={arXiv preprint arXiv:2501.10513},
year={ 2025 }
}