{"ID":5346714,"CreatedAt":"2026-06-30T04:09:55.830587294Z","UpdatedAt":"2026-07-02T14:44:57.46949413Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.30404","arxiv_id":"2606.30404","title":"HUMEMBR: Learning Human Routines for Predictive Embodied Navigation","abstract":"Understanding and navigating human-centered environments over extended periods of time while considering human behavior and routines remains a fundamental challenge in robotics. In real-world settings, robots may be asked to locate a specific individual, predict where that person is likely to be, or estimate when they typically leave a building. Addressing such queries requires reasoning over extensive histories of observations and capturing long-term behavioral patterns. To this end, we introduce Human-Centered Memory for Embodied Robots (HUMEMBR), a system designed for embodied question answering and routine-conditioned navigation. HUMEMBR integrates a continuous memory construction process with a parallel retrieval and querying mechanism, enabling the system to accumulate structured representations of human routines while supporting interactive, user-driven queries. Our experimental results indicate that HUMEMBR improves long-horizon reasoning about human behavior relative to full-context LLM baselines, while using substantially fewer tokens. Furthermore, we deploy HUMEMBR on a physical robot in two distinct environments, showing its ability to handle diverse queries and navigation tasks under real-world conditions.","short_abstract":"Understanding and navigating human-centered environments over extended periods of time while considering human behavior and routines remains a fundamental challenge in robotics. In real-world settings, robots may be asked to locate a specific individual, predict where that person is likely to be, or estimate when they...","url_abs":"https://arxiv.org/abs/2606.30404","url_pdf":"https://arxiv.org/pdf/2606.30404v1","authors":"[\"Samira Huber\",\"Klaas Pelzer\",\"Duc M. Nguyen\",\"Xuesu Xiao\",\"Sören Pirk\"]","published":"2026-06-29T14:50:50Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Large Language Model\"]","has_code":false}