EgoCogNav: Cognition-aware Human Egocentric Navigation

Modeling the cognitive and experiential factors of humannavigation is central to deepening our understanding of human–environmentinteraction and to enabling safe social navigation and effective assis-tive wayfinding. Most existing methods focus on forecasting motionsin fully observed scenes and often neglect human factors that capturehow people feel and respond to space. To address this gap, we proposeEgoCogNav, a multimodal egocentric navigation framework that jointlyforecasts perceived path uncertainty, trajectories and head motion fromegocentric video, gaze, and motion history. To facilitate research in thefield, we introduce the Cognition-aware Egocentric Navigation (CEN)dataset consisting of 6 hours real-world egocentric recordings capturingdiverse navigation behaviors in real-world scenarios. Experiments showthat EgoCogNav learns the perceived uncertainty that strongly correlateswith human-like behaviors such as scanning, hesitation, and backtrack-ing while improving trajectory and head-motion forecasting on held-outnavigation recordings. Project page: https://calvinzqiu.github.io/egocognav-project/