Abstract
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.
Abstract (translated)
在机器人领域,在任务执行过程中识别失败并实施恢复程序是非常具有挑战性的。传统方法依赖于大量数据或一组约束条件的可用性,而更现代的方法则利用大型语言模型(LLMs)来验证任务步骤并相应地重新规划。然而,这些方法通常需要离线操作,导致场景重置并产生高昂的成本。本文介绍了一个名为Recover的神经符号框架,用于在线故障识别和恢复。通过整合语义信息、逻辑规则和基于LLM的计划器,Recover利用符号信息增强LLMs生成恢复计划的能力,并降低相关成本。为了在模拟厨房环境中展示我们方法的性能,我们引入了OntoThor,一个描述AI2Thor仿真器设置的语义论。实证评估表明,OntoThor的逻辑规则准确地检测了分析任务中的所有故障,而Recover在故障检测和恢复方面都显著优于仅依赖LLM的基线方法。
URL
https://arxiv.org/abs/2404.00756
