Recover: A Neuro-Symbolic Framework for Robust Failure Detection and Recovery in Robotic Task Execution

Recover, a neuro-symbolic framework, leverages ontologies, logical rules, and large language models to efficiently detect failures during robotic task execution and generate recovery plans in real-time, enhancing the reliability and adaptability of autonomous systems.