# Ontology-Driven Robotic Specification Synthesis

arXiv: https://arxiv.org/abs/2602.05456 · HTML: https://mlsanity.com/paper/2602.05456/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 5 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [RobotGPT: Robot Manipulation Learning from ChatGPT](https://mlsanity.com/paper/2312.01421/) (2023)
- [Deploying and Evaluating LLMs to Program Service Mobile Robots](https://mlsanity.com/paper/2311.11183/) (2023)
- [Software Engineering for Robotics: Future Research Directions; Report from the 2023 Workshop on Software Engineering for Robotics](https://mlsanity.com/paper/2401.12317/) (2024)

## Abstract (arXiv)

This paper addresses robotic system engineering for safety- and mission-critical applications by bridging the gap between high-level objectives and formal, executable specifications. The proposed method, Robotic System Task to Model Transformation Methodology (RSTM2) is an ontology-driven, hierarchical approach using stochastic timed Petri nets with resources, enabling Monte Carlo simulations at mission, system, and subsystem levels. A hypothetical case study demonstrates how the RSTM2 method supports architectural trades, resource allocation, and performance analysis under uncertainty. Ontological concepts further enable explainable AI-based assistants, facilitating fully autonomous specification synthesis. The methodology offers particular benefits to complex multi-robot systems, such as the NASA CADRE mission, representing decentralized, resource-aware, and adaptive autonomous systems of the future.