Xiaoyue Sang
Papers
3
Total Citations
33
H-Index
3
About
Xiaoyue Sang is a robotics researcher specializing in biped locomotion, state estimation, and force-based control for humanoid robots. Her work centers on enabling robots to walk stably, recover from disturbances, and maintain precise awareness of their own posture. She has made significant contributions to model predictive control (MPC), visual-inertial odometry (VIO), and external force observation. Her most cited paper, "A learning-based model predictive control scheme and its application in biped locomotion" (2022, 13 citations), introduces a data-driven MPC approach that enhances walking stability by learning from dynamic interactions. In "Invariant Cubature Kalman Filtering-Based Visual-Inertial Odometry for Robot Pose Estimation" (2022, 12 citations), she addresses the critical challenge of rotational uncertainty in VIO, proposing a novel filter that improves pose estimation accuracy for robots navigating complex terrains. Her work "External force observer aided push recovery for torque-controlled biped robots" (2022, 8 citations) demonstrates how force estimation can enable real-time balance recovery, a key capability for robust humanoid locomotion. Sang’s research bridges control theory and practical robotics, offering scalable solutions for dynamic walking and autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3