S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning. - Daesol Cho; Dongseok Shim; H. Jin Kim (NeurIPS, 2022)

Unsupervised Reinforcement Learning for Transferable Manipulation Skill Discovery. - Daesol Cho; Jigang Kim; H. Jin Kim (IEEE Robotics Autom. Lett., 2022)

Automating Reinforcement Learning With Example-Based Resets. - Jigang Kim; Jae Hyeon Park; Daesol Cho; H. Jin Kim (IEEE Robotics Autom. Lett., 2022)

Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal Generation. - Daesol Cho; Seungjae Lee 002; H. Jin Kim (ICLR, 2023)

Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum. - Jigang Kim; Daesol Cho; H. Jin Kim (ICML, 2023)

CQM: Curriculum Reinforcement Learning with a Quantized World Model. - Seungjae Lee 002; Daesol Cho; Jonghae Park; H. Jin Kim (NeurIPS, 2023)

Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement. - Daesol Cho; Seungjae Lee 001; H. Jin Kim (NeurIPS, 2023)

CQM: Curriculum Reinforcement Learning with a Quantized World Model. - Seungjae Lee 001; Daesol Cho; Jonghae Park; H. Jin Kim (CoRR, 2023)