EgoCogNav: Cognition-aware Human Egocentric Navigation

TL;DR

EgoCogNav is a novel framework that models human-like navigation by predicting perceived uncertainty and integrating multimodal sensory data, advancing understanding of human-environment interaction and aiding social navigation.

Contribution

It introduces EgoCogNav, a cognition-aware navigation model that predicts perceived uncertainty and fuses scene features with sensory cues, along with the CEN dataset for research.

Findings

EgoCogNav predicts human-like behaviors such as scanning and hesitation.

The model generalizes well to unseen environments.

The CEN dataset provides extensive real-world egocentric navigation data.

Abstract

Modeling the cognitive and experiential factors of human navigation is central to deepening our understanding of human-environment interaction and to enabling safe social navigation and effective assistive wayfinding. Most existing methods focus on forecasting motions in fully observed scenes and often neglect human factors that capture how people feel and respond to space. To address this gap, We propose EgoCogNav, a multimodal egocentric navigation framework that predicts perceived path uncertainty as a latent state and jointly forecasts trajectories and head motion by fusing scene features with sensory cues. To facilitate research in the field, we introduce the Cognition-aware Egocentric Navigation (CEN) dataset consisting 6 hours of real-world egocentric recordings capturing diverse navigation behaviors in real-world scenarios. Experiments show that EgoCogNav learns the perceived uncertainty that highly correlates with human-like behaviors such as scanning, hesitation, and backtracking while generalizing to unseen environments.