Recover: A Neuro-Symbolic Framework for Failure Detection and Recovery
This addresses the challenge of costly, offline failure recovery in robotics, though it appears incremental as it builds on existing neuro-symbolic and LLM approaches.
The paper tackles the problem of online failure detection and recovery in robotics by introducing Recover, a neuro-symbolic framework that integrates ontologies, logical rules, and LLM-based planners. The result shows that Recover's logical rules accurately detect all failures in simulated kitchen tasks and considerably outperforms a baseline method reliant solely on LLMs for both failure detection and recovery.
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.