# UAV-ON: A Benchmark for Open-World Object Goal Navigation with Aerial Agents

arXiv: https://arxiv.org/abs/2508.00288 · HTML: https://mlsanity.com/paper/2508.00288/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 47 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [RoboTHOR: An Open Simulation-to-Real Embodied AI Platform](https://mlsanity.com/paper/2004.06799/) (2020)
- [AerialVLN: Vision-and-Language Navigation for UAVs](https://mlsanity.com/paper/2308.06735/) (2023)
- [Towards Realistic UAV Vision-Language Navigation: Platform, Benchmark, and Methodology](https://mlsanity.com/paper/2410.07087/) (2024)
- [PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNav](https://mlsanity.com/paper/2301.07302/) (2023)
- [Auxiliary Tasks and Exploration Enable ObjectNav](https://mlsanity.com/paper/2104.04112/) (2021)
- [Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching](https://mlsanity.com/paper/2311.12751/) (2023)
- [Stubborn: A Strong Baseline for Indoor Object Navigation](https://mlsanity.com/paper/2203.07359/) (2022)
- [Zero-shot object goal visual navigation](https://mlsanity.com/paper/2206.07423/) (2022)
- [Safe-VLN: Collision Avoidance for Vision-and-Language Navigation of Autonomous Robots Operating in Continuous Environments](https://mlsanity.com/paper/2311.02817/) (2023)
- [NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2411.08579/) (2024)
- [General Scene Adaptation for Vision-and-Language Navigation](https://mlsanity.com/paper/2501.17403/) (2025)
- [GeoNav: Empowering MLLMs with dual-scale geospatial reasoning for language-goal aerial navigation](https://mlsanity.com/paper/2504.09587/) (2025)
- [Aerial Vision-and-Language Navigation with Grid-based View Selection and Map Construction](https://mlsanity.com/paper/2503.11091/) (2025)
- [Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling](https://mlsanity.com/paper/2010.08596/) (2020)
- [Online Learning of Reusable Abstract Models for Object Goal Navigation](https://mlsanity.com/paper/2203.02583/) (2022)
- [Zero-Shot Object Searching Using Large-scale Object Relationship Prior](https://mlsanity.com/paper/2303.06228/) (2023)

## Built on by (most cited first, 30 of 33)

- [LongFly: Long-Horizon UAV Vision-and-Language Navigation with Spatiotemporal Context Integration](https://mlsanity.com/paper/2512.22010/) (2025)
- [iWorld-Bench: A Benchmark for Interactive World Models with a Unified Action Generation Framework](https://mlsanity.com/paper/2605.03941/) (2026)
- [UAVBench: An Open Benchmark Dataset for Autonomous and Agentic AI UAV Systems via LLM-Generated Flight Scenarios](https://mlsanity.com/paper/2511.11252/) (2025)
- [AerialVLA: A Vision-Language-Action Model for UAV Navigation via Minimalist End-to-End Control](https://mlsanity.com/paper/2603.14363/) (2026)
- [RAVEN: Resilient Aerial Navigation via Open-Set Semantic Memory and Behavior Adaptation](https://mlsanity.com/paper/2509.23563/) (2025)
- [α3-Bench: A Unified Benchmark of Safety, Robustness, and Efficiency for LLM-Based UAV Agents over 6G Networks](https://mlsanity.com/paper/2601.03281/) (2026)
- [Aerial Vision-Language Navigation with a Unified Framework for Spatial, Temporal and Embodied Reasoning](https://mlsanity.com/paper/2512.08639/) (2025)
- [APEX: A Decoupled Memory-based Explorer for Asynchronous Aerial Object Goal Navigation](https://mlsanity.com/paper/2602.00551/) (2026)
- [AirHunt: Bridging VLM Semantics and Continuous Planning for Efficient Aerial Object Navigation](https://mlsanity.com/paper/2601.12742/) (2026)
- [FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation](https://mlsanity.com/paper/2604.16298/) (2026)
- [ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue](https://mlsanity.com/paper/2605.01371/) (2026)
- [Embodied4C: Measuring What Matters for Embodied Vision-Language Navigation](https://mlsanity.com/paper/2512.18028/) (2025)
- [Are VLMs Lost Between Sky and Space? LinkS2Bench for UAV-Satellite Dynamic Cross-View Spatial Intelligence](https://mlsanity.com/paper/2604.02020/) (2026)
- [Last-Meter Precision Navigation for UAVs: A Diffusion-Refined Aerial Visual Servoing Approach](https://mlsanity.com/paper/2607.04352/) (2026)
- [FSD-VLN: Fast-Slow Dual-System Modeling for Aerial Long-Horizon Vision-Language Navigation](https://mlsanity.com/paper/2607.08359/) (2026)
- [α3-SecBench: A Large-Scale Evaluation Suite of Security, Resilience, and Trust for LLM-based UAV Agents over 6G Networks](https://mlsanity.com/paper/2601.18754/) (2026)
- [See-and-Reach: Precise Vision-Language Navigation for UAVs within the Field of View](https://mlsanity.com/paper/2606.20045/) (2026)
- [ActiveFly-Bench: Aligning Embodied Question Answering with Vision-Language-Action for Aerial Embodied Perception](https://mlsanity.com/paper/2607.10180/) (2026)
- [Uncertainty-Aware World Model for Aerial Image-Goal Navigation](https://mlsanity.com/paper/2608.05597/) (2026)
- [ScoutVLA: UAV-Centric Active Perception via a Dual-Expert VLA Model for Open-World Embodied Question Answering](https://mlsanity.com/paper/2606.14772/) (2026)
- [A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2607.09792/) (2026)
- [Parse, Search, and Confirmation: Training-Free Aerial Vision-and-Dialog Navigation with Chain-of-Thought Reasoning and Structured Spatial Memory](https://mlsanity.com/paper/2607.11529/) (2026)
- [AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning](https://mlsanity.com/paper/2601.15614/) (2026)
- [Listening with the Eyes: Benchmarking Egocentric Co-Speech Grounding across Space and Time](https://mlsanity.com/paper/2603.07966/) (2026)
- [How Far Are Large Multimodal Models from Human-Level Spatial Action? A Benchmark for Goal-Oriented Embodied Navigation in Urban Airspace](https://mlsanity.com/paper/2604.07973/) (2026)
- [DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV](https://mlsanity.com/paper/2607.21438/) (2026)
- [Zero-Shot Mission-Level Evaluation for Aerial MLLM Agents](https://mlsanity.com/paper/2607.22014/) (2026)
- [MulRobBench: A Decision-Level Benchmark for Safe and Security-Policy-Compliant Multimodal UAV Agents](https://mlsanity.com/paper/2607.23870/) (2026)
- [RecoverFly: A Failure-Aware Reinforcement Learning Post-Training Framework for Aerial Vision-Language Navigation](https://mlsanity.com/paper/2608.09467/) (2026)
- [AirAlign: Geometry-Aware Relative Pose Alignment for UAV Last-Meter Navigation](https://mlsanity.com/paper/2608.21926/) (2026)

## Abstract (arXiv)

Aerial navigation is a fundamental yet underexplored capability in embodied intelligence, enabling agents to operate in large-scale, unstructured environments where traditional navigation paradigms fall short. However, most existing research follows the Vision-and-Language Navigation (VLN) paradigm, which heavily depends on sequential linguistic instructions, limiting its scalability and autonomy. To address this gap, we introduce UAV-ON, a benchmark for large-scale Object Goal Navigation (ObjectNav) by aerial agents in open-world environments, where agents operate based on high-level semantic goals without relying on detailed instructional guidance as in VLN. UAV-ON comprises 14 high-fidelity Unreal Engine environments with diverse semantic regions and complex spatial layouts, covering urban, natural, and mixed-use settings. It defines 1270 annotated target objects, each characterized by an instance-level instruction that encodes category, physical footprint, and visual descriptors, allowing grounded reasoning. These instructions serve as semantic goals, introducing realistic ambiguity and complex reasoning challenges for aerial agents. To evaluate the benchmark, we implement several baseline methods, including Aerial ObjectNav Agent (AOA), a modular policy that integrates instruction semantics with egocentric observations for long-horizon, goal-directed exploration. Empirical results show that all baselines struggle in this setting, highlighting the compounded challenges of aerial navigation and semantic goal grounding. UAV-ON aims to advance research on scalable UAV autonomy driven by semantic goal descriptions in complex real-world environments.