Taeoh Ha — accepted AI-conference papers

2025

CDP: Constrained Diffusion Policies with Mirror Diffusion Model for Safety-Assured Imitation Learning

IROS 2025

This paper presents a novel imitation learning framework, called constrained diffusion policy (CDP). The primary objective of CDP is to ensure that learned policies strictly adhere to safety constraints while imitating expert demonstrations. To achieve this, we define a polytopic constraint that rep

Cited by 0 Source ↗Scholar ↗

2023

RIANet++: Road Graph and Image Attention Networks for Robust Urban Autonomous Driving Under Road Changes

Taeoh Ha, Jeongwoo Oh, Gunmin Lee, Jaeseok Heo, Do Hyung Kim, Byungkyu Park +2

RA-L 2023

The structure of roads plays an important role in designing autonomous driving algorithms. We propose a novel road graph based driving framework, named RIANet++. The proposed framework considers the road structural scene context by incorporating both graphical features of the road and visual informa

Cited by 4 Source ↗Scholar ↗