IMPRINT: Image-Conditioned Query Enrichment for Long-Tail Object Goal Navigation
- URL: http://arxiv.org/abs/2607.25106v1
- Date: Mon, 27 Jul 2026 22:09:32 GMT
- Title: IMPRINT: Image-Conditioned Query Enrichment for Long-Tail Object Goal Navigation
- Abstract summary: Embodied AI increasingly relies on queryable semantic maps built from pre-trained vision-language models to enable zero-shot Object Goal Navigation (ObjectNav)<n>We introduce IMPRINT, a zero-shot framework that enriches textual object queries with web-sourced images to improve grounding in queryable maps.<n>We present HSSD-rare, a new ObjectNav benchmark built on Habitat Synthetic Scenes featuring semantically specific subcategories.
- Score: 13.183204553352802
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Embodied AI increasingly relies on queryable semantic maps built from pre-trained vision-language models to enable zero-shot Object Goal Navigation (ObjectNav). However, existing approaches typically depend on text-only queries, which become less reliable as semantic specificity increases toward fine-grained object categories. We introduce IMPRINT, a zero-shot plug-and-play framework that enriches textual object queries with web-sourced images to improve grounding in queryable maps. Retrieved images are encoded using a vision-language model, matched against the semantic map to produce similarity maps, and aggregated to yield context-aware localization. Notably, this requires no training or modification of the underlying navigation policy. To explicitly evaluate long-tail behavior, we present HSSD-rare, a new ObjectNav benchmark built on Habitat Synthetic Scenes and featuring semantically specific subcategories. Across both OVON and HSSD-rare, image-conditioned queries consistently improve object grounding and yield end-to-end navigation gains. Further analysis reveals that translating localization gains to navigation performance depends critically on downstream detection quality, highlighting a key systems bottleneck in long-tail embodied navigation.
Related papers
- Instance-Enriched Semantic Maps for Visual Language Navigation [9.24074870738429]
Visual Language Navigation aims to enable an embodied agent to navigate complex environments by following natural language instructions.<n>Existing systems lack instance-level object detail and robustness to diverse user queries, limiting reliable navigation in complex indoor environments.<n>We propose Instance-Enriched Semantic Maps, a unified framework with three key contributions.
arXiv Detail & Related papers (2026-07-14T11:09:48Z) - Nav-$R^2$ Dual-Relation Reasoning for Generalizable Open-Vocabulary Object-Goal Navigation [67.68165784193556]
Nav-$R2$ is a framework that explicitly models two types of relationships, target-environment modeling and environment-action planning.<n>Our SA-Mem preserves the most target-relevant and current observation-relevant features from both temporal and semantic perspectives.<n>Nav-R2 achieves state-of-the-art performance in localizing unseen objects through a streamlined and efficient pipeline.
arXiv Detail & Related papers (2025-12-02T04:21:02Z) - MLFM: Multi-Layered Feature Maps for Richer Language Understanding in Zero-Shot Semantic Navigation [25.63797039823049]
LangNav is an open-vocabulary multi-object navigation dataset with natural language goal descriptions.<n> MLFM builds a queryable, multi-layered semantic map from pretrained vision-language features.<n>Experiments on LangNav show that MLFM outperforms state-of-the-art zero-shot mapping-based navigation baselines.
arXiv Detail & Related papers (2025-07-09T21:46:43Z) - Semantic Environment Atlas for Object-Goal Navigation [12.057544558656035]
We introduce the Semantic Environment Atlas (SEA), a novel mapping approach designed to enhance visual navigation capabilities of embodied agents.
The SEA integrates multiple semantic maps from various environments, retaining a memory of place-object relationships.
Our method achieves a success rate of 39.0%, an improvement of 12.4% over the current state-of-the-art, but also maintains robustness under noisy odometry and actuation conditions.
arXiv Detail & Related papers (2024-10-05T00:37:15Z) - Prioritized Semantic Learning for Zero-shot Instance Navigation [2.537056548731396]
We study zero-shot instance navigation, in which the agent navigates to a specific object without using object annotations for training.
We propose a Prioritized Semantic Learning (PSL) method to improve the semantic understanding ability of navigation agents.
Our PSL agent outperforms the previous state-of-the-art by 66% on zero-shot ObjectNav in terms of success rate and is also superior on the new InstanceNav task.
arXiv Detail & Related papers (2024-03-18T10:45:50Z) - Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation [88.84058353659107]
Instance ImageGoal Navigation (IIN) aims to navigate to a specified object depicted by a goal image in an unexplored environment.
We propose a new modular navigation framework named Instance-aware Exploration-Verification-Exploitation (IEVE) for instance-level image goal navigation.
Our method surpasses previous state-of-the-art work, with a classical segmentation model (0.684 vs. 0.561 success) or a robust model (0.702 vs. 0.561 success)
arXiv Detail & Related papers (2024-02-25T07:59:10Z) - Object Goal Navigation with Recursive Implicit Maps [92.6347010295396]
We propose an implicit spatial map for object goal navigation.
Our method significantly outperforms the state of the art on the challenging MP3D dataset.
We deploy our model on a real robot and achieve encouraging object goal navigation results in real scenes.
arXiv Detail & Related papers (2023-08-10T14:21:33Z) - Learning Navigational Visual Representations with Semantic Map
Supervision [85.91625020847358]
We propose a navigational-specific visual representation learning method by contrasting the agent's egocentric views and semantic maps.
Ego$2$-Map learning transfers the compact and rich information from a map, such as objects, structure and transition, to the agent's egocentric representations for navigation.
arXiv Detail & Related papers (2023-07-23T14:01:05Z) - Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language
Navigation [87.52136927091712]
We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions.
To achieve accurate and efficient navigation, it is critical to build a map that accurately represents both spatial location and the semantic information of the environment objects.
We propose a multi-granularity map, which contains both object fine-grained details (e.g., color, texture) and semantic classes, to represent objects more comprehensively.
arXiv Detail & Related papers (2022-10-14T04:23:27Z) - Semantic Image Alignment for Vehicle Localization [111.59616433224662]
We present a novel approach to vehicle localization in dense semantic maps using semantic segmentation from a monocular camera.
In contrast to existing visual localization approaches, the system does not require additional keypoint features, handcrafted localization landmark extractors or expensive LiDAR sensors.
arXiv Detail & Related papers (2021-10-08T14:40:15Z) - 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.