Portrait of Dmytro Kuzmenko

Dmytro Kuzmenko

Head of AI @ OnlyMonster·PhD Candidate @ NaUKMA·Visiting PhD @ AIRlab, UniTo
CV Scholar GitHub LinkedIn X

I am a researcher working in robotics, machine learning, and human-robot interaction, with a focus on efficient and reliable intelligent systems. I have experience conducting research and supervising student work, alongside developing AI systems in applied settings.

My work bridges methodological research and real-world implementation, with an emphasis on reinforcement learning and physical intelligence. I am actively engaged in international collaboration and regularly present my work at conferences. In addition to my research, I am committed to teaching, mentoring, and fostering a collaborative research environment.

If you want to reach out, don't hesitate to send me an email.

News

Selected Publications

MoIRA preprint thumbnail
MoIRA: Modular Instruction Routing Architecture for Multi-Task Robotics
D. Kuzmenko, N. Shvai
Neurocomputing, 2026
Full paper arXiv 
@article{KUZMENKO2026132962,
title = {MoIRA: Modular instruction routing architecture for multi-task robotics},
journal = {Neurocomputing},
volume = {674},
pages = {132962},
year = {2026},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2026.132962},
url = {https://www.sciencedirect.com/science/article/pii/S0925231226003590},
author = {Dmytro Kuzmenko and Nadiya Shvai},
}
EED Gym thumbnail
When Robots Say No: The Empathic Ethical Disobedience Benchmark
D. Kuzmenko, N. Shvai
ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2026
arXiv Code Website 
@article{kuzmenko2025eedgym,
          title   = {When Robots Say No: The Empathic Ethical Disobedience Benchmark},
          author  = {Kuzmenko, Dmytro and Shvai, Nadiya},
          year    = {2025},
          note    = {Accepted at the ACM/IEEE International Conference on Human-Robot Interaction (HRI 2026). Preprint: arXiv:2512.18474},
          doi     = {10.48550/arXiv.2512.18474},
        }
TD-MPC-Opt preprint thumbnail
TD-MPC-Opt — Distillation & Quantization for Efficient Multi-Task Model-Based RL
D. Kuzmenko, N. Shvai
Preprint (under review), 2025
arXiv Code 
@misc{kuzmenko2025tdmpcoptdistillingmodelbasedmultitask,
                      title={TD-MPC-Opt: Distilling Model-Based Multi-Task Reinforcement Learning Agents},
                      author={Dmytro Kuzmenko and Nadiya Shvai},
                      year={2025},
                      eprint={2507.01823},
                      archivePrefix={arXiv},
                      primaryClass={cs.LG},
                      url={https://arxiv.org/abs/2507.01823},
                }
AAMAS 2025 thumbnail
Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning
D. Kuzmenko, N. Shvai
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2025
Full paper arXiv 
@inproceedings{10.5555/3709347.3743949,
                  author = {Kuzmenko, Dmytro and Shvai, Nadiya},
                  title = {Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning},
                  year = {2025},
                  isbn = {9798400714269},
                  publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
                  address = {Richland, SC},
                  booktitle = {Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems},
                  pages = {2597-2599},
                  numpages = {3},
                  keywords = {efficient rl agents, knowledge distillation, model compression, model-based reinforcement learning, multi-task learning},
                  location = {Detroit, MI, USA},
                  series = {AAMAS '25}
                }
View all publications (8)

Academic Service

Teaching

Senior Lecturer, NaUKMA
13 undergraduate theses supervised

Courses: