Seonil Lee — accepted AI-conference papers

2025

RDMM: Enhancing Household Robotics with On-Device Contextual Memory and Decision Making

Shady Nasrat, Minseong Jo, Seonil Lee, Myungsu Kim, Jiho Lee, Yeoncheol Jang +1

IROS 2025

Large language models (LLMs) represent a significant advancement in integrating physical robots with AI-driven systems. In this research, we present a framework that leverages Robotics Decision-Making Models (RDMM) for decision-making in domain-specific contexts, enhancing robotic autonomy. This fra

Cited by 0 Source ↗code ↗Scholar ↗

2022

Autonomous Exploration in a Cluttered Environment for a Mobile Robot With 2D-Map Segmentation and Object Detection

Hyungseok Kim, Hyeongjin Kim, Seonil Lee, Hyeonbeom Lee

RA-L 2022

Frontier-based exploration is widely adopted for exploring an unknown region. The conventional frontier-based exploration for a mobile robot may collide with three-dimensional (3D) obstacles or can suffer from a slower exploration time because the robot may move to another place before completely ex

Cited by 26 Source ↗Scholar ↗