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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.
Blog Posts
I am a partner writer on Medium. I blog about my research and other topics on Medium:
Past Projects
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