5 papers
FIRE-VLM: A Vision-Language-Driven Reinforcement Learning Framework for UAV Wildfire Tracking in a Physics-Grounded Fire Digital Twin
Chris Webb, Mobin Habibpour, Mayamin Hamid Raha +3
Wildfire monitoring demands autonomous systems capable of reasoning under extreme visual degradation, rapidly evolving physical dynamics, and scarce real-world training data. Exist…
Think, Remember, Navigate: Zero-Shot Object-Goal Navigation with VLM-Powered Reasoning
Mobin Habibpour, Fatemeh Afghah
While Vision-Language Models (VLMs) are set to transform robotic navigation, existing methods often underutilize their reasoning capabilities. To unlock the full potential of VLMs…
FIRETWIN: Digital Twin Advancing Multi-Modal Sensing, Interactive Analytics for Wildfire Response
Mayamin Hamid Raha, Ali Reza Tavakkoli, Chris Webb +4
Current wildfire management systems lack integrated virtual environments that combine historical data with immersive digital representations, hindering deep analysis and effective…
History-Augmented Vision-Language Models for Frontier-Based Zero-Shot Object Navigation
Mobin Habibpour, Fatemeh Afghah
Object Goal Navigation (ObjectNav) challenges robots to find objects in unseen environments, demanding sophisticated reasoning. While Vision-Language Models (VLMs) show potential,…
Seeing Heat with Color -- RGB-Only Wildfire Temperature Inference from SAM-Guided Multimodal Distillation using Radiometric Ground Truth
Michael Marinaccio, Fatemeh Afghah
High-fidelity wildfire monitoring using Unmanned Aerial Vehicles (UAVs) typically requires multimodal sensing - especially RGB and thermal imagery - which increases hardware cost a…