Sensing, Social, and Motion Intelligence in Embodied Navigation: A Comprehensive Survey
Chaoran Xiong, Yulong Huang, Fangwen Yu, Changhao Chen, Yue Wang, Songpengchen Xia, Ling Pei

TL;DR
This survey comprehensively reviews embodied navigation, emphasizing sensing, social, and motion intelligence, structured through the TOFRA framework, highlighting current advancements, challenges, and evaluation metrics in the field.
Contribution
It introduces the TOFRA framework for structured analysis of embodied navigation and critically reviews current platforms, metrics, and open research challenges.
Findings
Summarizes the state of the art in embodied navigation
Identifies key open challenges in sensing, social, and motion intelligence
Provides a structured framework (TOFRA) for future research
Abstract
Embodied navigation (EN) advances traditional navigation by enabling robots to perform complex egocentric tasks through sensing, social, and motion intelligence. In contrast to classic methodologies that rely on explicit localization and pre-defined maps, EN leverages egocentric perception and human-like interaction strategies. This survey introduces a comprehensive EN formulation structured into five stages: Transition, Observation, Fusion, Reward-policy construction, and Action (TOFRA). The TOFRA framework serves to synthesize the current state of the art, provide a critical review of relevant platforms and evaluation metrics, and identify critical open research challenges. A list of studies is available at https://github.com/Franky-X/Awesome-Embodied-Navigation.
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Taxonomy
TopicsSocial Robot Interaction and HRI · Action Observation and Synchronization · Multimodal Machine Learning Applications
