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cs.RO2025
Tidiness Score-Guided Monte Carlo Tree Search for Visual Tabletop Rearrangement
Hogun Kee, Wooseok Oh, Minjae Kang +2
In this paper, we present the tidiness score-guided Monte Carlo tree search (TSMCTS), a novel framework designed to address the tabletop tidying up problem using only an RGB-D came…
cs.RO2021
Semi-Supervised Imitation Learning with Mixed Qualities of Demonstrations for Autonomous Driving
Gunmin Lee, Wooseok Oh, Seungyoun Shin +5
In this paper, we consider the problem of autonomous driving using imitation learning in a semi-supervised manner. In particular, both labeled and unlabeled demonstrations are leve…
cs.RO2021
Towards Defensive Autonomous Driving: Collecting and Probing Driving Demonstrations of Mixed Qualities
Jeongwoo Oh, Gunmin Lee, Jeongeun Park +8
Designing or learning an autonomous driving policy is undoubtedly a challenging task as the policy has to maintain its safety in all corner cases. In order to secure safety in auto…