Nat Dilokthanakul
About
Nat is a postdoctoral researcher at the school of information science and technology, VISTEC. His research focuses on the designs of autonomous and adaptive agents. The goal of his work is to enable robots to work in uncontrolled environments and alongside humans. He co-leads multiple industrial projects within IST, including pipe-inspection robot projects, a robot-data platform, and a service robotic project.
Research
Collaborative Agents
We are interested in machine learning algorithms that enable agents to be collaborative. An agent with collaborative skills can reason about other agents' state-of-mind and adapts its behaviour accordingly.
Human-Machine Collaboration and Communication
A better human-AI interface will make it much easier to operate a robot in complex tasks.
Adaptive Locomotion Control
A robot with a robust locomotion controller can operate in unforgiving domains by adapting its behaviours. Is there a generic and simple adaptivity rule that enable such behaviours?
Visual Representation Learning, Navigation and Manipulation
Navigation and Manipulation require high-level cognitive abilities. What is the best representation for these kind of reasoning tasks?
Journals
- Sawadwuthikul, G., Tothong, T., Lodkaew, T., Soisudarat, P., Nutanong, S., Manoonpong, P., Dilokthanakul, N., 2021. Visual Goal Human-Robot Communication Framework with Few-Shot Learning: a Case Study in Robot Waiter System, IEEE Transactions on Industrial Informatics. (pdf) (code) (bib)
- Banluesombatkul, N., Ouppaphan, P., Leelaarporn, P., Lakhan, P., Chaitusaney, B., Jaimchariya, N., Chuangsuwanich, E., Chen, W., Phan, H., *Dilokthanakul, N. and *Wilaiprasitporn, T., 2020. Metasleeplearner: A pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using meta-learning. IEEE Journal of Biomedical and Health Informatics. (*co-corresponding) (pdf) (code) (bib)
- Dilokthanakul, N. , Kaplanis, C., Pawlowski, N. and Shanahan, M., 2019. Feature control as intrinsic motivation for hierarchical reinforcement learning. IEEE transactions on neural networks and learning systems, 30(11), pp.3409-3418. (pdf) (code) (bib)
Conferences
- Chuthong T., Leung B., Tiraborisute K., Ngamkajornwiwat P., Manoonpong P. and Dilokthanakul N., 2020. Dynamical State Forcing on Central Pattern Generators for Efficient Robot Locomotion Control. In Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science, vol 12533. Springer, Cham. (pdf) (code) (bib)
- Charakorn R., Manoonpong P. and Dilokthanakul N., 2020. Investigating Partner Diversification Methods in Cooperative Multi-agent Deep Reinforcement Learning. In Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. (pdf) (code) (bib)
- Dilokthanakul, N. and Shanahan, M., 2018. Deep reinforcement learning with risk-seeking exploration. In International Conference on Simulation of Adaptive Behavior (pp. 201-211). Springer, Cham. (pdf) (code) (bib)
Preprints and Workshops
- Charakorn, R., Manoonpong, P., Dilokthanakul, N. , 2021. Learning to Cooperate with Unseen Agents Through Meta-Reinforcement Learning, The 20th International Conference on Autonomous Agents and Multiagent Systems. AAMAS 2021. (Extended abstract)
- Charakorn, R., Thawornwattana, Y., Itthipuripat, S., Pawlowski, N., Manoonpong, P. and Dilokthanakul, N., 2020. An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection. arXiv preprint arXiv:2001.08957. (pdf) (code) (bib)
- Dilokthanakul, N. , Mediano, P.A., Garnelo, M., Lee, M.C., Salimbeni, H., Arulkumaran, K. and Shanahan, M., 2016. Deep unsupervised clustering with gaussian mixture variational autoencoders. arXiv preprint arXiv:1611.02648. (pdf) (code) (bib)
- Arulkumaran, K., Dilokthanakul, N. , Shanahan, M. and Bharath, A.A., 2016. Classifying options for deep reinforcement learning. arXiv preprint arXiv:1604.08153. (pdf) (code) (bib)