# AirHunt: Bridging VLM Semantics and Continuous Planning for Efficient Aerial Object Navigation

arXiv: https://arxiv.org/abs/2601.12742 · HTML: https://mlsanity.com/paper/2601.12742/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 28 of its 39 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 28 of 28)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [ProcTHOR: Large-Scale Embodied AI Using Procedural Generation](https://mlsanity.com/paper/2206.06994/) (2022)
- [ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning](https://mlsanity.com/paper/2309.16650/) (2023)
- [ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects](https://mlsanity.com/paper/2006.13171/) (2020)
- [VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation](https://mlsanity.com/paper/2312.03275/) (2023)
- [ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation](https://mlsanity.com/paper/2301.13166/) (2023)
- [L3MVN: Leveraging Large Language Models for Visual Target Navigation](https://mlsanity.com/paper/2304.05501/) (2023)
- [PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNav](https://mlsanity.com/paper/2301.07302/) (2023)
- [FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://mlsanity.com/paper/2010.08196/) (2020)
- [Semantic Visual Navigation by Watching YouTube Videos](https://mlsanity.com/paper/2006.10034/) (2020)
- [How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers](https://mlsanity.com/paper/2305.16925/) (2023)
- [FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning](https://mlsanity.com/paper/2010.11561/) (2020)
- [Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight](https://mlsanity.com/paper/1907.01531/) (2019)
- [UAV-ON: A Benchmark for Open-World Object Goal Navigation with Aerial Agents](https://mlsanity.com/paper/2508.00288/) (2025)
- [An Autonomous Drone for Search and Rescue in Forests using Airborne Optical Sectioning](https://mlsanity.com/paper/2105.04328/) (2021)
- [FIESTA: Fast Incremental Euclidean Distance Fields for Online Motion Planning of Aerial Robots](https://mlsanity.com/paper/1903.02144/) (2019)
- [UAV-Flow Colosseo: A Real-World Benchmark for Flying-on-a-Word UAV Imitation Learning](https://mlsanity.com/paper/2505.15725/) (2025)
- [ApexNav: An Adaptive Exploration Strategy for Zero-Shot Object Navigation with Target-centric Semantic Fusion](https://mlsanity.com/paper/2504.14478/) (2025)
- [See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned Aerial Navigation](https://mlsanity.com/paper/2509.22653/) (2025)
- [RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System](https://mlsanity.com/paper/2209.08533/) (2022)
- [FALCON: Fast Autonomous Aerial Exploration using Coverage Path Guidance](https://mlsanity.com/paper/2407.00577/) (2024)
- [Object Goal Navigation using Data Regularized Q-Learning](https://mlsanity.com/paper/2208.13009/) (2022)
- [FlySearch: Exploring how vision-language models explore](https://mlsanity.com/paper/2506.02896/) (2025)
- [Star-Searcher: A Complete and Efficient Aerial System for Autonomous Target Search in Complex Unknown Environments](https://mlsanity.com/paper/2402.16348/) (2024)
- [EPIC: A Lightweight LiDAR-Based UAV Exploration Framework for Large-Scale Scenarios](https://mlsanity.com/paper/2410.14203/) (2024)

## Built on by (most cited first, 4 of 4)

- [Evaluating Multimodal LLMs as Generalist Vision-Language-Action Agents for Drone Control: Commanding, Approaching, Tracking and Searching](https://mlsanity.com/paper/2609.01404/) (2026)
- [VLN on the Fly: An Onboard Vision-Language Navigation Stack for Aerial Robots](https://mlsanity.com/paper/2609.20191/) (2026)
- [Structured World-State Reasoning for Agentic Robotic Search](https://mlsanity.com/paper/2609.23841/) (2026)
- [AerialDojo-200K: A Large-Scale Benchmark Suite for Open-World Aerial Object-Goal Search](https://mlsanity.com/paper/2609.36066/) (2026)

## Abstract (arXiv)

Recent advances in large Vision-Language Models (VLMs) have provided rich semantic understanding that empowers drones to search for open-set objects via natural language instructions. However, prior systems struggle to integrate VLMs into practical aerial systems due to orders-of-magnitude frequency mismatch between VLM inference and real-time planning, as well as VLMs' limited 3D scene understanding. They also lack a unified mechanism to balance semantic guidance with motion efficiency in large-scale environments. To address these challenges, we present AirHunt, an aerial object navigation system that efficiently locates open-set objects with zero-shot generalization in outdoor environments by seamlessly fusing VLM semantic reasoning with continuous path planning. AirHunt features a dual-pathway asynchronous architecture that establishes a synergistic interface between VLM reasoning and path planning, enabling continuous flight with adaptive semantic guidance that evolves through motion. Moreover, we propose an active dual-task reasoning module that exploits geometric and semantic redundancy to enable selective VLM querying, and a semantic-geometric coherent planning module that dynamically reconciles semantic priorities and motion efficiency in a unified framework, enabling seamless adaptation to environmental heterogeneity. We evaluate AirHunt across diverse object navigation tasks and environments, demonstrating a higher success rate with lower navigation error and reduced flight time compared to state-of-the-art methods. Real-world experiments further validate AirHunt's practical capability in complex and challenging environments. Code and dataset will be made publicly available before publication.