Justin Tang

The University of Sydney, Australian Centre for Robotic Vision

Papers

Total Citations

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About

Justin Tang is a leading researcher in nonlinear control theory and dynamic legged locomotion, whose work bridges rigorous mathematical frameworks with experimental validation. His primary research areas include control contraction metrics, invariant funnel synthesis, and underactuated robotic walking. Tang’s most influential contribution is his 2017 paper “Unifying Robot Trajectory Tracking with Control Contraction Metrics” (30 citations), which provides a foundational framework for certifying stability in nonlinear robotic systems. In a second highly cited work (17 citations), he addresses the challenging problem of constructing invariant funnels for dynamic walking robots, using transverse dynamics and sum-of-squares verification to guarantee stable walking from a set of initial conditions—a key advance for reliable bipedal locomotion. Tang also contributed to open-source robotics with a 2017 paper (3 citations) detailing the design and modeling of a low-cost compass-gait bipedal platform, enabling reproducible dynamic walking experiments. His work is notable for combining theoretical depth with practical validation, offering students and researchers a clear path from abstract control theory to real-world robotic performance.