In the evolving field of robotics, the challenge of Object Navigation (ON) in household environments has attracted significant interest. Existing ON benchmarks typically place objects in locations guided by general scene priors, without accounting for the specific placement habits of individual users. This omission limits the adaptability of navigation agents in personalized household environments. To address this, we introduce User-centric Object Navigation (UcON), a new benchmark that incorporates user-specific object placement habits, referred to as user habits. This benchmark requires agents to leverage these user habits for more informed decision-making during navigation. UcON encompasses approximately 22,600 user habits across 489 object categories. UcON is, to our knowledge, the first benchmark that explicitly formalizes and evaluates habit-conditioned object navigation at scale and covers the widest range of target object categories. Additionally, we propose a habit retrieval module to extract and utilize habits related to target objects, enabling agents to infer their likely locations more effectively. Experimental results demonstrate that current SOTA methods exhibit substantial performance degradation under habit-driven object placement, while integrating user habits consistently improves success rates. Code is available at https://github.com/whcpumpkin/User-Centric-Object-Navigation.

翻译：在快速发展的机器人学领域，家庭环境中的目标导航挑战引起了广泛关注。现有的目标导航基准通常依据通用场景先验知识来放置物体，而未考虑个体用户的具体放置习惯。这一缺失限制了导航智能体在个性化家庭环境中的适应性。为解决此问题，我们提出了以用户为中心的目标导航，这是一个融合了用户特定物体放置习惯的新基准，这些习惯被称为用户习惯。该基准要求智能体在导航过程中利用这些用户习惯进行更明智的决策。UcON涵盖了489个物体类别中约22,600条用户习惯。据我们所知，UcON是首个大规模明确形式化并评估习惯条件化目标导航的基准，并覆盖了最广泛的目标物体类别。此外，我们提出了一个习惯检索模块，用于提取和利用与目标物体相关的习惯，使智能体能够更有效地推断其可能位置。实验结果表明，当前最先进的方法在习惯驱动的物体放置场景下表现出显著的性能下降，而整合用户习惯则能持续提高成功率。代码发布于 https://github.com/whcpumpkin/User-Centric-Object-Navigation。