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

arXiv: https://arxiv.org/abs/1903.03920 · HTML: https://mlsanity.com/paper/1903.03920/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2019.

## Built on by (most cited first, 7 of 7)

- [Software Architecture for ML-based Systems: What Exists and What Lies Ahead](https://mlsanity.com/paper/2103.07950/) (2021)
- [From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation](https://mlsanity.com/paper/2101.02647/) (2021)
- [On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems](https://mlsanity.com/paper/2103.10194/) (2021)
- [Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning](https://mlsanity.com/paper/2103.10847/) (2021)
- [Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems](https://mlsanity.com/paper/2204.06254/) (2022)
- [Auto-COP: Adaptation Generation in Context-Oriented Programming using Reinforcement Learning Options](https://mlsanity.com/paper/2103.06757/) (2021)
- [RobEthiChor: Automated Context-aware Ethics-based Negotiation for Autonomous Robots](https://mlsanity.com/paper/2507.22664/) (2025)

## Abstract (arXiv)

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.