Yuhwan Kwon — accepted AI-conference papers

2022

Physically Consistent Preferential Bayesian Optimization for Food Arrangement

Yuhwan Kwon, Yoshihisa Tsurumine, Takeshi Shimmura, Sadao Kawamura, Takamitsu Matsubara

RA-L 2022

This letter considers the problem of estimating a preferred food arrangement for users from interactive pairwise comparisons using Computer Graphics (CG)-based dish images. As a foodservice industry requirement, we need to utilize domain rules for the geometry of the arrangement (e.g., the food layo

Cited by 1 Source ↗Scholar ↗

2022

Randomized-to-Canonical Model Predictive Control for Real-World Visual Robotic Manipulation

Tomoya Yamanokuchi, Yuhwan Kwon, Yoshihisa Tsurumine, Eiji Uchibe, Jun Morimoto, Takamitsu Matsubara

RA-L 2022

Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must be collected once to perform the sim-to-real transfer, which remains a significant human effort in transferring the model

Cited by 5 Source ↗Scholar ↗