Chan Woo Kim — accepted AI-conference papers

2026

Natural Functional Gradients for Smooth Trajectory Optimization

Kisang Park, Chan Woo Kim, Kyungjae Lee, Sungjoon Choi

RSS 2026poster

Generating collision-free and smoothly executable motions is a persistent challenge in robotic manipulation, especially in cluttered workspaces and narrow passages where the feasible set is highly nonconvex and fragmented. We propose a trajectory optimization method that performs geometry-aware upda…

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2023

Sequential Preference Ranking for Efficient Reinforcement Learning from Human Feedback

Minyoung Hwang, Gunmin Lee, Hogun Kee, Chan Woo Kim, Kyungjae Lee, Songhwai Oh

NeurIPS 2023poster

Reinforcement learning from human feedback (RLHF) alleviates the problem of designing a task-specific reward function in reinforcement learning by learning it from human preference. However, existing RLHF models are considered inefficient as they produce only a single preference data from each human…

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