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Aerial Vision-and-Dialog Navigation
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The ability to converse with humans and follow natural language commands is crucial for intelligent unmanned aerial vehicles (a.k.a. drones). It can relieve people's burden of holding a controller all the time, allow multitasking, and make drone control more accessible for people with disabilities or with their hands occupied. To this end, we introduce Aerial Vision-and-Dialog Navigation (AVDN), to navigate a drone via natural language conversation. We build a drone simulator with a continuous photorealistic environment and collect a new AVDN dataset of over 3k recorded navigation trajectories with asynchronous human-human dialogs between commanders and followers. The commander provides initial navigation instruction and further guidance by request, while the follower navigates the drone in the simulator and asks questions when needed. During data collection, followers' attention on the drone's visual observation is also recorded. Based on the AVDN dataset, we study the tasks of aerial navigation from (full) dialog history and propose an effective Human Attention Aided Transformer model (HAA-Transformer), which learns to predict both navigation waypoints and human attention.
Forward citations
Cited by 5 Pith papers
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Knowing the Self, Understanding the World: A Dual-Cognition Benchmark for UAV Spatio-temporal Reasoning with MLLMs
UAV-DualCog evaluates multimodal LLMs on self-state and environment-state reasoning in aerial images and videos, and shows current models are unreliable at spatial and temporal grounding.
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Taking Flight with Dialogue: Enabling Natural Language Control for PX4-based Drone Agent
An open-source ROS2/PX4 framework using locally hosted LLMs and VLMs enables natural language drone commands, with the best simulated mission success rate at 40%.
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Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation
A dual-branch text-imagination system, with one LLM branch for state estimation and one for candidate-direction description, improves R2R navigation success over prior LLM-based VLN methods.
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UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation
UAV-VLA generates drone flight plans from natural language using satellite imagery, GPT, and Molmo, and introduces a 30-image benchmark, but its evaluation against a single human operator is weak.
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No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation
Test-time scaling—parallel candidate generation, iterative self-correction, and multi-criteria selection—improves a frozen UAV navigation VLM's success rate by about 2 percentage points on the TravelUAV benchmark.
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