Research on Scene Perception and Task Semantic Understanding for Indoor Service Robots: Based on Literature Review and Case Analysis

Abstract

With the advancement of artificial intelligence and robotics, indoor service robots are gradually being deployed in complex environments including residences and hospitals to perform diverse tasks. However, relying solely on traditional localization and obstacle avoidance capabilities can no longer meet the complex demands of real-world scenarios. Robots must possess semantic-level understanding of environmental objects, spatial relationships, and service objectives. Currently, environmental representation for indoor robots is undergoing a transition from being geometry-dominated to semantic-enhanced. This study centers on scene perception and task semantic understanding for indoor service robots. It aims to explore how robots build a systematic cognition of scenes, objects, user commands and task workflows based on low-level visual and spatial data. Using literature review and case analysis methods, this paper synthesizes representative achievements in fields such as semantic mapping, semantic navigation, semantic SLAM, explicit knowledge representation, and task planning. The study argues that scene perception and task semantic understanding constitute a continuous intelligent chain from "environment recognition" to "task execution". In the future, indoor service robots require further improvements in multimodal information fusion, knowledge-driven modeling and task reasoning for open scenarios, so as to enhance their practical performance and operational reliability.