Mintaek Oh — accepted AI-conference papers

2025

E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models

Chan Kim, Keonwoo Kim, Mintaek Oh, Hanbi Baek, Jiyang Lee, Donghwi Jung +7

ICRA 2025

Large language models (LLMs) have shown significant potential in guiding embodied agents to execute language instructions across a range of tasks, including robotic manipulation and navigation. However, existing methods are primarily designed for static environments and do not leverage the agent's o

Cited by 5 Source ↗code ↗Scholar ↗

2025

Language as Cost: Proactive Hazard Mapping using VLM for Robot Navigation

Mintaek Oh, Chan Kim, Seung-Woo Seo, Seong-Woo Kim

IROS 2025

Robots operating in human-centric or hazardous environments must proactively anticipate and mitigate dangers beyond basic obstacle detection. Traditional navigation systems often depend on static maps, which struggle to account for dynamic risks, such as a person emerging from a suddenly opening doo

Cited by 3 Source ↗code ↗Scholar ↗