User-Centric Object Navigation: A Benchmark with Integrated User Habits for Personalized Embodied Object Search

2026-02-06

AI Summary

This work addresses the limitation of existing object navigation benchmarks, which overlook individual users’ idiosyncratic object placement habits, thereby hindering agents’ generalization in personalized home environments. To bridge this gap, we introduce UcON, the first large-scale benchmark for personalized object navigation, encompassing 489 target object categories and approximately 22,600 user-specific placement habits, and formally define the habit-driven navigation task. We propose a habit retrieval module that extracts object-related placement patterns from user history to guide the agent in inferring likely object locations. Experimental results demonstrate that current state-of-the-art methods suffer significant performance degradation under habit-driven layouts, whereas incorporating user habits consistently improves navigation success rates, underscoring the critical role of personalized priors in intelligent navigation.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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 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.

Problem

• Object Navigation
• User Habits
• Personalized Robotics
• Embodied AI
• Household Environments

Innovation

• User-Centric Navigation
• Object Navigation
• User Habits
• Habit Retrieval
• Personalized Embodied AI