Dual-LLM Hierarchical Task Planning and Skill Grounding for Mobile Manipulation in Long-Horizon Restroom Cleaning

Public restroom cleaning is a laborious but critical task for public hygiene, offering both an opportunity and a challenge for robotic automation. Traditional robotic systems struggle in such environments due to partial observability, dynamic conditions, and reliance on rigid task scripts and brittle perception pipelines. This paper presents a hierarchical task planning and skill grounding framework leveraging large language models (LLMs) for autonomous restroom cleaning using a mobile manipulator. A high-level LLM (LLM-H) generates semantic task plans based on symbolic scene representations derived from a visionlanguage model (VLM), while a low-level LLM (LLM-L) refines these plans into a sequence of executable, parameterized robotic skills. To ensure robust and adaptive operation, the framework integrates a semantic active perception module that mitigates visual occlusions through targeted exploration, a dynamic task buffer that reprioritizes tasks in real time to handle environmental changes, and a VLM-based validation module that assesses task outcomes and triggers corrective actions when necessary. Experiments conducted in real-world public restrooms demonstrate that our system outperforms state-of-the-art baselines in task-completion rate while robustly handling partial observability, dynamic events, and long-horizon cleaning sequences. Its modular, scalable approach provides a solid foundation for deploying intelligent service robots in hygiene-critical public spaces.