Privacy-Aware LLMs-Assisted Task Planning for Home Robots

Abstract

Multi-modal large language models (LLMs) are expected to significantly enhance the intelligence of home service robots. However, reliance on cloud processing of raw visual data poses critical privacy risks. To address this problem, we propose a novel two-stage cloud-edge hybrid architecture for robots in domestic environments. This architecture employs a lightweight local LLM to perform sensitive content screening and semantic abstraction before transmitting the data to a more powerful cloud-based LLM for high-level planning and reasoning. Experiments with our end-to-end system demonstrate that it effectively protects a wide range of private data with minimal impact on task success rates. Without modifying cloud models, our approach offers a deployable performance–privacy trade-off for home robots, advancing safe and socially acceptable autonomy.