@article{choi2026wip,title={Under review},author={Choi, Chanyeok and Lee, Youngmoon},journal={Preprint},year={2026},note={Details withheld during review},keywords={robot learning}}
We study reward-poisoning attacks on cooperative multi-agent reinforcement learning, where an attacker agent participates in the same environment as the cooperative crawler agents and places high-reward lure points that redirect the crawlers off-trajectory. On a Unity 50×50 m benchmark we evaluate PPO and SAC in both single- and multi-agent settings, observing cumulative-reward drops of 18.7% (multi-agent PPO) and 20.9% (multi-agent SAC), and up to 98.1% for single-agent SAC. We argue the asymmetry is structural: PPO’s on-policy clipping locks the policy into the first sampled lure region, while SAC’s off-policy replay buffer dilutes poison samples — except at small buffer sizes.
@inproceedings{choi2025poisoning,title={Poisoning Attacks on Multi-Agent Reinforcement Learning Systems},author={Choi, Chanyeok and Cho, Jaehwan and Lee, Youngmoon},booktitle={IEEE-RAS International Conference on Humanoid Robots (Humanoids), Late-Breaking Report},year={2025},keywords={multi-agent reinforcement learning, adversarial attacks, reward poisoning, PPO, SAC, humanoid robots}}
2024
ICPR
HAPtics: Human Action Prediction in Real-time via Pose Kinematics
Niaz Ahmad, Saif Ullah, Jawad Khan, Chanyeok Choi, and Youngmoon Lee
In International Conference on Pattern Recognition (ICPR), 2024
@inproceedings{ahmad2024haptics,title={{HAPtics}: Human Action Prediction in Real-time via Pose Kinematics},author={Ahmad, Niaz and Ullah, Saif and Khan, Jawad and Choi, Chanyeok and Lee, Youngmoon},booktitle={International Conference on Pattern Recognition (ICPR)},address={Kolkata, India},year={2024},doi={10.1007/978-3-031-78354-8_10},keywords={human pose, action prediction, vision}}
@inproceedings{choi2024drone,title={Causes and Fixes of Unexpected Drone Shutoffs},author={Choi, Hojun and Choi, Chanyeok and Lee, Youngmoon},booktitle={ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED)},year={2024},doi={10.1145/3665314.3670847},keywords={drones, reliability, field robotics}}
Snapbot is a manipulator-based photography system that frames, composes, and captures stylized portraits in real time, treating the human subject as a dynamic interaction partner rather than a static target.
@inproceedings{choi2024snapbot,title={{Snapbot}: Enabling Dynamic Human-Robot Interactions for Real-Time Computational Photography},author={Choi, Chanyeok and Lee, Youngmoon},booktitle={ACM/IEEE International Conference on Human-Robot Interaction (HRI), Late-Breaking Report},year={2024},keywords={human-robot interaction, manipulation, computational photography}}
2023
Preprint
Leveraging Keypoints as Dynamic Centroids for Unified Representation of Human Pose and Instance Segmentation
Niaz Ahmad, Jawad Khan, Chanyeok Choi, Youngmoon Lee, and Kang G. Shin
@article{ahmad2023keypoints,title={Leveraging Keypoints as Dynamic Centroids for Unified Representation of Human Pose and Instance Segmentation},author={Ahmad, Niaz and Khan, Jawad and Choi, Chanyeok and Lee, Youngmoon and Shin, Kang G.},journal={Preprint},year={2023},note={Withdrawn from CVPR 2024},keywords={human pose, instance segmentation, keypoints}}
ICDM-W
SSK-DNN: Semantic and Sentiment Knowledge for Incremental Text Sentiment Classification
Jawad Khan, Niaz Ahmad, Chanyeok Choi, Saif Ullah, Gyu Rin Kim, and Youngmoon Lee
In IEEE ICDM Workshop on Incremental Learning (IncrLearn), 2023
@inproceedings{khan2023ssk,title={{SSK-DNN}: Semantic and Sentiment Knowledge for Incremental Text Sentiment Classification},author={Khan, Jawad and Ahmad, Niaz and Choi, Chanyeok and Ullah, Saif and Kim, Gyu Rin and Lee, Youngmoon},booktitle={IEEE ICDM Workshop on Incremental Learning (IncrLearn)},address={Shanghai, China},year={2023},keywords={natural language processing, sentiment classification, incremental learning}}