# Taking Recoveries to Task: Recovery-Driven Development for Recipe-based Robot Tasks

arXiv: https://arxiv.org/abs/2001.10386 · HTML: https://mlsanity.com/paper/2001.10386/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery](https://mlsanity.com/paper/2101.01625/) (2021)
- [Semantic-Based Explainable AI: Leveraging Semantic Scene Graphs and Pairwise Ranking to Explain Robot Failures](https://mlsanity.com/paper/2108.03554/) (2021)
- [Towards Robust One-shot Task Execution using Knowledge Graph Embeddings](https://mlsanity.com/paper/2105.04484/) (2021)
- [MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile Manipulation](https://mlsanity.com/paper/2510.18316/) (2025)

## Abstract (arXiv)

Robot task execution when situated in real-world environments is fragile. As such, robot architectures must rely on robust error recovery, adding non-trivial complexity to highly-complex robot systems. To handle this complexity in development, we introduce Recovery-Driven Development (RDD), an iterative task scripting process that facilitates rapid task and recovery development by leveraging hierarchical specification, separation of nominal task and recovery development, and situated testing. We validate our approach with our challenge-winning mobile manipulator software architecture developed using RDD for the FetchIt! Challenge at the IEEE 2019 International Conference on Robotics and Automation. We attribute the success of our system to the level of robustness achieved using RDD, and conclude with lessons learned for developing such systems.