# Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots

## TL;DR

This paper presents a novel approach combining machine learning and planning to enable autonomous robots to self-adapt efficiently by finding optimal configurations in complex, changing environments, improving robustness and performance over long deployments.

## Contribution

It introduces a method that uses machine learning to identify Pareto-optimal configurations, reducing the planning complexity for self-adaptive robotic systems.

## Key findings

- High-quality adaptation plans generated in uncertain environments
- Effective reduction of planning complexity through ML-guided configuration search
- Demonstrated robustness in adversarial scenarios

## Abstract

Modern cyber-physical systems (e.g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time. Assumptions about parts of the system made at design time may not hold at run time, especially when a system is deployed for long periods (e.g., over decades). Self-adaptation is designed to find reconfigurations of systems to handle such run-time inconsistencies. Planners can be used to find and enact optimal reconfigurations in such an evolving context. However, for systems that are highly configurable, such planning becomes intractable due to the size of the adaptation space. To overcome this challenge, in this paper we explore an approach that (a) uses machine learning to find Pareto-optimal configurations without needing to explore every configuration and (b) restricts the search space to such configurations to make planning tractable. We explore this in the context of robot missions that need to consider task timeliness and energy consumption. An independent evaluation shows that our approach results in high-quality adaptation plans in uncertain and adversarial environments.