Yizhang Liu
Papers
3
Total Citations
19
H-Index
3
About
Yizhang Liu is a robotics researcher specializing in humanoid locomotion, manipulation, and real-time motion planning. His work bridges theoretical control and practical robot autonomy, with key contributions in redundancy resolution, push recovery, and obstacle avoidance. Liu’s most cited paper, “Human-like redundancy resolution: An integrated inverse kinematics scheme for anthropomorphic manipulators with radial elbow offset” (2022, 12 citations), introduces a novel approach that mimics human arm motion for more natural and efficient robotic manipulation. In “Reachability-based Push Recovery for Humanoid Robots with Variable-Height Inverted Pendulum” (2021, 4 citations), he developed a Hamilton-Jacobi reachability analysis to compute zero-step capturability, enabling humanoid robots to maintain balance under external perturbations—a critical advance for dynamic walking. His work on “A Dynamical System Approach to Real-time Three-Dimensional Concave Obstacle Avoidance” (2020, 3 citations) tackles the challenging problem of navigating around complex, non-convex obstacles by decomposing them into intersecting ellipsoids, allowing real-time avoidance in cluttered environments. Though early in his career, Liu’s integrated, human-inspired approaches are shaping safer, more agile robots for real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3