User-Centric Object Navigation: A Benchmark with Integrated User Habits for Personalized Embodied Object Search
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911427363602432 |
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| author | Wang, Hongcheng Zhu, Jinyu Dong, Hao |
| author_facet | Wang, Hongcheng Zhu, Jinyu Dong, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06459 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | User-Centric Object Navigation: A Benchmark with Integrated User Habits for Personalized Embodied Object Search Wang, Hongcheng Zhu, Jinyu Dong, Hao Robotics 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. |
| title | User-Centric Object Navigation: A Benchmark with Integrated User Habits for Personalized Embodied Object Search |
| topic | Robotics |
| url | https://arxiv.org/abs/2602.06459 |