User-Centric Object Navigation: A Benchmark with Integrated User Habits for Personalized Embodied Object Search
Hongcheng Wang, Jinyu Zhu, Hao Dong

TL;DR
This paper introduces UcON, a new benchmark for personalized object navigation in household environments that incorporates user-specific object placement habits, and demonstrates that leveraging these habits improves navigation success.
Contribution
The paper presents the first habit-conditioned object navigation benchmark with extensive user habit data and a habit retrieval module to enhance navigation performance.
Findings
Current SOTA methods perform poorly with habit-driven object placement.
Integrating user habits significantly improves navigation success rates.
UcON covers the widest range of object categories among similar benchmarks.
Abstract
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…
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Taxonomy
TopicsSocial Robot Interaction and HRI · Multimodal Machine Learning Applications · Robot Manipulation and Learning
