# RoboRun: A Robot Runtime to Exploit Spatial Heterogeneity

arXiv: https://arxiv.org/abs/2108.13354 · HTML: https://mlsanity.com/paper/2108.13354/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2021.

## Built on by (most cited first, 3 of 3)

- [Shared Memory-contention-aware Concurrent DNN Execution for Diversely Heterogeneous System-on-Chips](https://mlsanity.com/paper/2308.05869/) (2023)
- [Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles](https://mlsanity.com/paper/2205.03347/) (2022)
- [TAPAS: Throughput-adaptive Perception for Autonomous Systems](https://mlsanity.com/paper/2607.17317/) (2026)

## Abstract (arXiv)

The limited onboard energy of autonomous mobile robots poses a tremendous challenge for practical deployment. Hence, efficient computing solutions are imperative. A crucial shortcoming of state-of-the-art computing solutions is that they ignore the robot's operating environment heterogeneity and make static, worst-case assumptions. As this heterogeneity impacts the system's computing payload, an optimal system must dynamically capture these changes in the environment and adjust its computational resources accordingly. This paper introduces RoboRun, a mobile-robot runtime that dynamically exploits the compute-environment synergy to improve performance and energy. We implement RoboRun in the Robot Operating System (ROS) and evaluate it on autonomous drones. We compare RoboRun against a state-of-the-art static design and show 4.5X and 4X improvements in mission time and energy, respectively, as well as a 36% reduction in CPU utilization.