Research​

My research lies at the intersection of deep learning, dynamical systems, and robotics. I develop algorithms for combining deep learning with first-principles physics models. I explore how these modeling algorithms can facilitate robotic tasks such as model-based control, through simulation and hardware validations.

[5] T.  Z.  Jiahao,  K. Y. Chee,  and  M. A. Hsieh,  “Online Dynamics Learning for Predictive Control with an Application to Aerial Robots,” Conference on Robot Learning (CoRL), 2022.

[4] Y. Wu, T. Z. Jiahao, J. Wang, P. A. Yushkevich, M. A. Hsieh, and J. C. Gee, "NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

[3] K. Y. Chee, T. Z. Jiahao, and M. A. Hsieh, “Knode-mpc: A knowledge-based  data-driven  predictive  control  framework  for  aerial  robots,” IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 2, pp. 2819–2826, 2022.

[2] T. Z. Jiahao, L. Pan, and M. A. Hsieh, "Learning to swarm with knowledge-based neural ordinary differential equations," IEEE International Conference on Robotics and Automation (ICRA), 2022.

[1] T. Z. Jiahao,  M. A. Hsieh, and  E. Forgoston, “Knowledge-based learning of nonlinear dynamics and chaos,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 31, no. 11,p. 111101, 2021.