Recover: A Neuro-Symbolic Framework for Failure Detection and Recovery
Cristina Cornelio Mohammed Diab
Recognizing failures during task execution and implementing recovery
procedures is challenging in robotics. Traditional approaches rely on the
availability of extensive data or a tight set of constraints, while more recent
approaches leverage large language models (LLMs) to verify task steps and
replan accordingly. However, these methods often operate offline, necessitating
scene resets and incurring in high costs. This paper introduces Recover, a
neuro-symbolic framework for online failure identification and recovery. By
integrating ontologies, logical rules, and LLM-based planners, Recover exploits
symbolic information to enhance the ability of LLMs to generate recovery plans
and also to decrease the associated costs. In order to demonstrate the
capabilities of our method in a simulated kitchen environment, we introduce
OntoThor, an ontology describing the AI2Thor simulator setting. Empirical
evaluation shows that OntoThor's logical rules accurately detect all failures
in the analyzed tasks, and that Recover considerably outperforms, for both
failure detection and recovery, a baseline method reliant solely on LLMs.