Sensing, Social, and Motion Intelligence in Embodied Navigation: A Comprehensive Survey

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.