Publications

State-Covering Trajectory Stitching for Diffusion Planners

Kyowoon Lee and Jaesik Choi

Conference on Neural Information Processing Systems (NeurIPS), 2025.Generative modelsReinforcement learning

📄Paper

Local Manifold Approximation and Projection for Manifold-Aware Diffusion Planning

Kyowoon Lee and Jaesik Choi

International Conference on Machine Learning (ICML), 2025.Generative modelsReinforcement learning

📄Paper

Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learning

Jihyeon Seong, Sekwang Oh and Jaesik Choi

International Joint Conference on Artificial Intelligence (IJCAI), 2024.Time seriesReinforcement learning

📄Paper

Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans

Kyowoon Lee*, Seongun Kim* and Jaesik Choi

Conference on Neural Information Processing Systems (NeurIPS), 2023 (* contributed equally).Generative modelsReinforcement learning

📄Paper

Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills

Seongun Kim*, Kyowoon Lee* and Jaesik Choi

International Conference on Machine Learning (ICML), 2023 (* contributed equally).Reinforcement learning

📄Paper

Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning

Kyowoon Lee*, Seongun Kim* and Jaesik Choi

International Conference on Robotics and Automation (ICRA), 2023 (* contributed equally) (pdf).Explainable AIReinforcement learning

📄Paper

Cooperative Multi-Robot Task Allocation with Reinforcement Learning

Bumjin Park, Cheongwoong Kang, Jaesik Choi

Applied Sciences, 12 (1), 2022.Reinforcement learning

📄Paper

Explaining the Decisions of Deep Policy Networks for Robotic Manipulations

Seongun Kim and Jaesik Choi

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.Explainable AIReinforcement learning

📄Paper

A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection

YongHyeok Seo, Dongju Shin, Jaesik Choi and Se Young Chun

International Conference on Robotics and Automation (ICRA), 2020.Reinforcement learning

📄Paper

XAI—Explainable artificial intelligence

David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf and Guang-Zhong Yang

Science Robotics, 4(37), 2019. (IF: 19.4)Explainable AIReinforcement learning

📄Paper

Markov Information Bottleneck to Improve Information Flow in Stochastic Neural Networks

Thanh T. Nguyen and Jaesik Choi

Entropy, 21(10), 976, 2019.Reinforcement learning

📄Paper

Implementation of End-to-End Training of Deep Visuomotor Policies for Manipulation of a Robotic Arm of Baxter Research Robot

Seongun Kim, Sol-A Kim, Rafael Lima, Jaesik Choi

Journal of Korea Robotics Society, 2019.Reinforcement learning

📄Paper

Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling

Kyowoon Lee*, Sol-A Kim*, Jaesik Choi and Seong-Whan Lee

International Conference on Machine Learning (ICML), 2018 (* contributed equally).Reinforcement learning

📄Paper

Safe navigation of a mobile robot considering visibility of environment

Woojin Chung, Seokgyu Kim, Minki Choi, Jaesik Choi, Hoyeon Kim, Changbae Moon and Jae-bok Song

IEEE Transactions on Industrial Electronics, 56 (10), 2009.Reinforcement learning

📄Paper

Combining Planning and Motion Planning

Jaesik Choi, Eyal Amir

IEEE International Conference on Robotics and Automation (ICRA), 2009 (pdf).Reinforcement learning

📄Paper

Factor-Guided Motion Planning for a Robot Arm

Jaesik Choi and Eyal Amir

IEEE International Conference on Intelligent Robots and Systems (IROS), 2007 (pdf).Reinforcement learning

📄Paper

Efficient navigation of mobile robot based on the robot’s experience in human co-existing environment

Jaesik Choi, Woojin Chung and Jae-Bok Song

International Conference on Control, Automation and Systems (ICCAS), 2005.Reinforcement learning