Environment Modeling for Service Robots From a Task Execution Perspective

TL;DR

The paper addresses the challenge of enabling service robots to operate autonomously in open, dynamic home environments by proposing a task-execution-oriented framework for environment modeling. It surveys localization, navigation, manipulation, and long-term autonomy (LTA) methods across $2$-$D$, $3$-$D$, and semantic representations, analyzing each approach's strengths, limitations, and suitability for domestic tasks. Key contributions include a structured taxonomy of methods, comparative analyses across modeling paradigms, and a discussion of current challenges with concrete future directions such as sensor fusion, lifelong learning, NeRF-based representations, and the integration of vision foundation models. The work highlights the practical significance of developing adaptable, task-driven environment models to achieve reliable, long-term autonomy for home service robots in real-world, dynamic settings.

Abstract

Service robots are increasingly entering the home to provide domestic tasks for residents. However, when working in an open, dynamic, and unstructured home environment, service robots still face challenges such as low intelligence for task execution and poor long-term autonomy (LTA), which has limited their deployment. As the basis of robotic task execution, environment modeling has attracted significant attention. This integrates core technologies such as environment perception, understanding, and representation to accurately recognize environmental information. This paper presents a comprehensive survey of environmental modeling from a new task-executionoriented perspective. In particular, guided by the requirements of robots in performing domestic service tasks in the home environment, we systematically review the progress that has been made in task-execution-oriented environmental modeling in four respects: 1) localization, 2) navigation, 3) manipulation, and 4) LTA. Current challenges are discussed, and potential research opportunities are also highlighted.

Figures (9)

• Figure 1: Basic process for robot task execution. A prerequisite for home service robots to perform domestic tasks is to model the environment as a 2-D, 3-D, semantic, or hybrid map to support robot localization, navigation, and manipulation wang2017autonomoussunderhauf2017meaningfulzhang2021building.
• Figure 2: Four parts of robot task-execution-oriented environment modeling.
• Figure 3: Typical process for robot localization.
• Figure 4: LRF measurement characteristics. (a) Three possible range measurements (i.e., $d$, $d'$ and $d"$) for glass walls kim2016localization. (b) Glass can only be effectively detected within a limited angle close to its surface normal jiang2020online.
• Figure 5: Basic process for robot navigation.