Yukai Gong
Papers
13
Total Citations
459
H-Index
9
About
Yukai Gong is a robotics researcher whose work sits at the intersection of bipedal locomotion control, dynamical modeling, and motion planning for legged robots. His research has made substantial contributions to the theoretical and practical foundations of feedback control for bipedal walking, with a particular emphasis on angular momentum-based models and low-dimensional abstractions that bridge elegant theory with real-world robotic performance. Gong's most-cited work (216 citations) demonstrated feedback control of the Cassie bipedal robot — a widely adopted academic platform from Agility Robotics — enabling walking, standing, and even Segway riding through virtual constraints and gait libraries. This work helped establish Cassie as a benchmark for locomotion research. He subsequently developed and validated the Angular Momentum Linear Inverted Pendulum (ALIP) model, arguing persuasively that angular momentum about the contact point offers richer predictive power than traditional center-of-mass velocity approaches, a contribution spanning multiple influential papers. His terrain-adaptive controllers using Model Predictive Control and virtual constraints extended these ideas to uneven and dynamically moving surfaces, including ship-like swaying platforms. Beyond locomotion, Gong has explored safety-aware informative motion planning using Control Barrier Functions. With over 450 cumulative citations, his work meaningfully advances the design of agile, reliable bipedal robots capable of operating in complex real-world environments.
Research Focus
Key Achievements
Top Papers
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- 6Toward Safety-Aware Informative Motion Planning for Legged Robots22 citations · 2021
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