HUMEMBR: 人間のルーチンを学習する予測的身体性ナビゲーション
HUMEMBR: Learning Human Routines for Predictive Embodied Navigation
人間の行動パターンを長期記憶として蓄積し、質問応答やルーチンに基づくナビゲーションを行うシステムを提案。実ロボットでの検証も行い、トークン数を抑えつつ長期的推論を改善した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Samira Huber, Klaas Pelzer, Duc M. Nguyen, Xuesu Xiao, Sören Pirk
分類: cs.RO
原文アブストラクト
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.
