# GAMap: Zero-Shot Object Goal Navigation with Multi-Scale Geometric-Affordance Guidance

arXiv: https://arxiv.org/abs/2410.23978 · HTML: https://mlsanity.com/paper/2410.23978/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2024.

## Built on by (most cited first, 23 of 23)

- [g3D-LF: Generalizable 3D-Language Feature Fields for Embodied Tasks](https://mlsanity.com/paper/2411.17030/) (2024)
- [LongNav-R1: Horizon-Adaptive Multi-Turn RL for Long-Horizon VLA Navigation](https://mlsanity.com/paper/2602.12351/) (2026)
- [History-Augmented Vision-Language Models for Frontier-Based Zero-Shot Object Navigation](https://mlsanity.com/paper/2506.16623/) (2025)
- [Humanoid Agent via Embodied Chain-of-Action Reasoning with Multimodal Foundation Models for Zero-Shot Loco-Manipulation](https://mlsanity.com/paper/2504.09532/) (2025)
- [Hierarchical Scoring with 3D Gaussian Splatting for Instance Image-Goal Navigation](https://mlsanity.com/paper/2506.07338/) (2025)
- [LGR: LLM-Guided Ranking of Frontiers for Object Goal Navigation](https://mlsanity.com/paper/2503.20241/) (2025)
- [HyPerNav: Hybrid Perception for Object-Oriented Navigation in Unknown Environment](https://mlsanity.com/paper/2510.22917/) (2025)
- [PSG-Nav: Probabilistic Scene Graph Navigation via Multiverse Decision Making](https://mlsanity.com/paper/2606.01313/) (2026)
- [PIGEON: VLM-Driven Object Navigation via Points of Interest Selection](https://mlsanity.com/paper/2511.13207/) (2025)
- [Schr"odinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation](https://mlsanity.com/paper/2512.21201/) (2025)
- [TrajRAG: Retrieving Geometric-Semantic Experience for Zero-Shot Object Navigation](https://mlsanity.com/paper/2605.01700/) (2026)
- [EvoMemNav: Efficient Self-Evolving Fine-Grained Memory for Zero-Shot Embodied Navigation](https://mlsanity.com/paper/2606.03509/) (2026)
- [SR-Nav: Spatial Relationships Matter for Zero-shot Object Goal Navigation](https://mlsanity.com/paper/2603.18443/) (2026)
- [REST: Receding Horizon Explorative Steiner Tree for Zero-Shot Object-Goal Navigation](https://mlsanity.com/paper/2603.18624/) (2026)
- [Multi-Scale Gaussian-Language Map for Zero-shot Embodied Navigation and Reasoning](https://mlsanity.com/paper/2605.01736/) (2026)
- [EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation](https://mlsanity.com/paper/2606.18634/) (2026)
- [ViTL: Temporal Logic-Guided Zero-Shot Natural Language Navigation via Vision-Language Models](https://mlsanity.com/paper/2606.30696/) (2026)
- [HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models](https://mlsanity.com/paper/2607.13072/) (2026)
- [NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation](https://mlsanity.com/paper/2607.19695/) (2026)
- [SSTG-Nav: Metric-Grounded Spatial-Semantic Topological Graphs for Reusable Object Navigation](https://mlsanity.com/paper/2608.00527/) (2026)
- [RTNav: Towards Real-Time Zero-Shot Object Navigation](https://mlsanity.com/paper/2608.26496/) (2026)
- [Affordance-Conditioned Decision Making: Bridging the Semantic-Spatial Gap in Zero-Shot Cross-Floor Vision-and-Language Navigation](https://mlsanity.com/paper/2609.32292/) (2026)
- [GG-Nav: Goal-oriented Grounding Chain-of-Thought Elicits Reasoning in Navigation Foundation Models](https://mlsanity.com/paper/2610.14541/) (2026)

## Abstract (arXiv)

Zero-Shot Object Goal Navigation (ZS-OGN) enables robots or agents to navigate toward objects of unseen categories without object-specific training. Traditional approaches often leverage categorical semantic information for navigation guidance, which struggles when only objects are partially observed or detailed and functional representations of the environment are lacking. To resolve the above two issues, we propose textitGeometric-part and Affordance Maps (GAMap), a novel method that integrates object parts and affordance attributes as navigation guidance. Our method includes a multi-scale scoring approach to capture geometric-part and affordance attributes of objects at different scales. Comprehensive experiments conducted on HM3D and Gibson benchmark datasets demonstrate improvements in Success Rate and Success weighted by Path Length, underscoring the efficacy of our geometric-part and affordance-guided navigation approach in enhancing robot autonomy and versatility, without any additional object-specific training or fine-tuning with the semantics of unseen objects and/or the locomotions of the robot.