User-Centric Object Navigation: A Benchmark with Integrated User Habits for Personalized Embodied Object Search
- URL: http://arxiv.org/abs/2602.06459v1
- Date: Fri, 06 Feb 2026 07:43:05 GMT
- Title: User-Centric Object Navigation: A Benchmark with Integrated User Habits for Personalized Embodied Object Search
- Abstract summary: We introduce User-centric Object Navigation (UcON), a new benchmark that incorporates user-specific object placement habits.<n>UcON encompasses approximately 22,600 user habits across 489 object categories.<n>We propose a habit retrieval module to extract and utilize habits related to target objects, enabling agents to infer their likely locations more effectively.
- Score: 11.501877446575612
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation [0.0]
To locate a target object while exploring the unknown environment is a fundamental capability for autonomous agents.<n>In Nav, successful arrival at the target object provides a basic measure of performance.<n>In unknown environments, the key to efficient navigation lies in deciding where to explore next.
arXiv Detail & Related papers (2026-06-17T03:04:11Z) - PIGEON: VLM-Driven Object Navigation via Points of Interest Selection [50.77437819030925]
We propose PIGEON: Point of Interest Guided Exploration for Object Navigation with Visual-Language Model (VLM)<n>We use a large Visual-Language Model (VLM), named PIGEON-VL, to select Points of Interest (PoI) formed during exploration and then employ a lower-level planner for action output, increasing the decision frequency.<n>Experiments on classic object navigation benchmarks demonstrate that our zero-shot transfer method achieves state-of-the-art performance, while RLVR further enhances the model's semantic guidance capabilities, enabling deep reasoning during real-time navigation.
arXiv Detail & Related papers (2025-11-17T10:19:13Z) - Personalized Instance-based Navigation Toward User-Specific Objects in Realistic Environments [44.6372390798904]
We propose a new task denominated Personalized Instance-based Navigation (PIN), in which an embodied agent is tasked with locating and reaching a specific personal object.<n>In each episode, the target object is presented to the agent using two modalities: a set of visual reference images on a neutral background and manually annotated textual descriptions.
arXiv Detail & Related papers (2024-10-23T18:01:09Z) - GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigation [65.71524410114797]
GOAT-Bench is a benchmark for the universal navigation task GO to AnyThing (GOAT)
In GOAT, the agent is directed to navigate to a sequence of targets specified by the category name, language description, or image.
We benchmark monolithic RL and modular methods on the GOAT task, analyzing their performance across modalities.
arXiv Detail & Related papers (2024-04-09T20:40:00Z) - TAS: A Transit-Aware Strategy for Embodied Navigation with Non-Stationary Targets [55.09248760290918]
We present a novel algorithm for navigation in dynamic scenarios with non-stationary targets.<n>Our novel Transit-Aware Strategy (TAS) enriches embodied navigation policies with object path information.<n> TAS improves performance in non-stationary environments by rewarding agents for synchronizing their routes with target routes.
arXiv Detail & Related papers (2024-03-14T22:33:22Z) - Think, Act, and Ask: Open-World Interactive Personalized Robot Navigation [17.279875204729553]
Zero-Shot Object Navigation (ZSON) enables agents to navigate towards open-vocabulary objects in unknown environments.
We introduce ZIPON, where robots need to navigate to personalized goal objects while engaging in conversations with users.
We propose Open-woRld Interactive persOnalized Navigation (ORION) to make sequential decisions to manipulate different modules for perception, navigation and communication.
arXiv Detail & Related papers (2023-10-12T01:17:56Z) - ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object
Navigation [75.13546386761153]
We present a novel zero-shot object navigation method, Exploration with Soft Commonsense constraints (ESC)
ESC transfers commonsense knowledge in pre-trained models to open-world object navigation without any navigation experience.
Experiments on MP3D, HM3D, and RoboTHOR benchmarks show that our ESC method improves significantly over baselines.
arXiv Detail & Related papers (2023-01-30T18:37:32Z) - Object Goal Navigation using Goal-Oriented Semantic Exploration [98.14078233526476]
This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments.
We propose a modular system called, Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently.
arXiv Detail & Related papers (2020-07-01T17:52:32Z) - ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to
Objects [119.46959413000594]
This document summarizes the consensus recommendations of a working group on ObjectNav.
We make recommendations on subtle but important details of evaluation criteria.
We provide a detailed description of the instantiation of these recommendations in challenges organized at the Embodied AI workshop at CVPR 2020.
arXiv Detail & Related papers (2020-06-23T17:18:54Z) - Learning hierarchical relationships for object-goal navigation [7.074818959144171]
We present Memory-utilized Joint hierarchical Object Learning for Navigation in Indoor Rooms (MJOLNIR)
MJOLNIR is a target-driven navigation algorithm, which considers the inherent relationship between target objects, and the more salient contextual objects occurring in its surrounding.
Our model learns to converge much faster than other algorithms, without suffering from the well-known overfitting problem.
arXiv Detail & Related papers (2020-03-15T04:01:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.