Papers
5
Total Citations
35
H-Index
3
About
Dongyun Kang is redefining how legged robots move through the world. His research sits at the intersection of model predictive control, contact dynamics, and robust locomotion, with a focus on enabling quadruped robots to navigate complex, unstructured terrain without relying on pre-planned contact sequences. Kang’s most impactful contribution is his contact-implicit MPC framework, which uses differential dynamic programming to discover multi-contact motions in real time—a breakthrough that eliminates the need for predefined footholds or gait patterns. This work, published in 2024, has already garnered 22 citations, signaling its rapid influence in the field. He has also pioneered an online friction coefficient identification method for slippery terrain, and developed a modular residual learning framework that fuses model-based and learning-based approaches for robust locomotion. By relaxing complementarity constraints in his DDP formulations, Kang has made contact-implicit control computationally tractable for real-time applications. His work is not just advancing theory—it is building the practical tools needed for legged robots to walk, run, and adapt in the real world.
Research Focus
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Top Papers
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