# Automated Robot Recovery from Assumption Violations of High-Level Specifications

arXiv: https://arxiv.org/abs/2407.00562 · HTML: https://mlsanity.com/paper/2407.00562/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [INPROVF: Leveraging Large Language Models to Repair High-level Robot Controllers from Assumption Violations](https://mlsanity.com/paper/2503.13660/) (2025)

## Abstract (arXiv)

This paper presents a framework that enables robots to automatically recover from assumption violations of high-level specifications during task execution. In contrast to previous methods relying on user intervention to impose additional assumptions for failure recovery, our approach leverages synthesis-based repair to suggest new robot skills that, when implemented, repair the task. Our approach detects violations of environment safety assumptions during the task execution, relaxes the assumptions to admit observed environment behaviors, and acquires new robot skills for task completion. We demonstrate our approach with a Hello Robot Stretch in a factory-like scenario.