Tom Z. Jiahao
Papers
4
Total Citations
98
H-Index
3
About
Tom Z. Jiahao is a leading researcher at the intersection of robotics, control theory, and machine learning, with a primary focus on developing intelligent, data-driven frameworks for aerial and multi-agent systems. His most impactful contribution is the KNODE-MPC framework (81 citations), a pioneering knowledge-based data-driven predictive control approach that integrates physical insights with neural networks to enhance quadrotor performance under uncertainty. Jiahao has also advanced online dynamics learning for model predictive control, enabling aerial robots to adapt their models in real-time for improved closed-loop behavior. In swarm robotics, he introduced Knowledge-Based Neural ODEs to learn decentralized collective dynamics from complex agent interactions, offering a principled path to scalable robot swarms. His recent work on learning switching Port-Hamiltonian systems with uncertainty quantification addresses fundamental challenges in modeling hybrid physical systems, with applications ranging from robotic locomotion to power electronics. Through these contributions, Jiahao has established himself as a key figure in bridging model-based control and learning, with his work cited across robotics, control, and artificial intelligence communities.
Research Focus
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