5 papers
Which Terrain Is Better? Preference Learning with VLM Prototypes for Off-Road Traversability Ranking
Ji-Hoon Hwang, Jisung Bae, E-In Son +3
In vision-based off-road navigation, a robot needs to know not only which obstacles to avoid but also which terrain is better. The first is handled by freespace detection or semant…
Feeling Terrain Before Crossing: World Models for Off-Road Navigation
E-In Son, Dong-Wook Kim, Ji-Hoon Hwang +4
Navigation world models plan by foresight, predicting the future that each candidate action sequence produces and selecting the best, rather than mapping observations to actions di…
FPAS: Frontier-Based Path Planning with Adaptive Sampling for Large-Scale Unknown Environments
Jinwoo Choi, Yeonkyu Lee, Jung-Taak Kim +2
In this work, we propose Frontier-based Path Planning with Adaptive Sampling (FPAS), a novel framework designed for efficient goal-reaching in large-scale, unknown environments. Wh…
From General Vision to Reliable Traversability Estimation: Adapting Vision Foundation Models for Unstructured Outdoor Environments
Ji-Hoon Hwang, Jisung Bae, Dong-Wook Kim +2
Vision-based approaches have become the dominant paradigm for traversability estimation in unstructured outdoor environments, typically adapting vision foundation models (VFMs) via…
Phantom Paradoxes: Language Models Treat Unresolved Circular Truth Dependence as Contradiction
Ji Ho Bae
Do language models struggle with self-reference itself, or with structures where propagating truth values along references cannot settle a value in finitely many steps? Our results…