ROSA: A Knowledge-based Solution for Robot Self-Adaptation

Autonomous robots must operate in diverse environments and handle multiple tasks despite uncertainties. This creates challenges in designing software architectures and task decision-making algorithms, as different contexts may require distinct task logic and architectural configurations. To address this, robotic systems can be designed as self-adaptive systems capable of adapting their task execution and software architecture at runtime based on theirthis http URLpaper introduces ROSA, a novel knowledge-based framework for RObot Self-Adaptation, which enables task-and-architecture co-adaptation (TACA) in robotic systems. ROSA achieves this by providing a knowledge model that captures all application-specific knowledge required for adaptation and by reasoning over this knowledge at runtime to determine when and how adaptation should occur. In addition to a conceptual framework, this work provides an open-source ROS 2-based reference implementation of ROSA and evaluates its feasibility and performance in an underwater robotics application. Experimental results highlight ROSA's advantages in reusability and development effort for designing self-adaptive robotic systems.

@article{silva2025_2505.00733,
  title={ ROSA: A Knowledge-based Solution for Robot Self-Adaptation },
  author={ Gustavo Rezende Silva and Juliane Päßler and S. Lizeth Tapia Tarifa and Einar Broch Johnsen and Carlos Hernández Corbato },
  journal={arXiv preprint arXiv:2505.00733},
  year={ 2025 }
}