Papers
15
Total Citations
243
H-Index
8
About
Songyan Xin is a robotics researcher whose work spans legged locomotion, motion planning, and robotic manipulation, with a particular focus on enabling dynamic, versatile movement in complex robotic systems. His research has made significant contributions to the control of humanoid, biped, and quadrupedal robots, developing sophisticated frameworks that allow these machines to walk, run, jump, and navigate real-world environments with remarkable robustness and adaptability. Among his most influential contributions is a planning and control framework for wheeled biped robots that bridges the efficiency of wheeled motion with the adaptability of legged locomotion, garnering 50 citations. His work on footstep optimization using Model Predictive Control — cited 36 times — and robust quadrupedal locomotion through LQR control (39 citations) demonstrate his expertise in applying advanced mathematical techniques to pressing challenges in robot dynamics. Xin also co-developed the OCRTOC benchmark (46 citations), a widely recognized cloud-based platform advancing robotic grasping and manipulation research globally. His earlier work on straight-leg walking strategies and neural-network-controlled spring-mass templates reflects a consistent drive to close the gap between theoretical biomechanical models and practical robotic implementation. Across his career, Xin has established himself as a thoughtful and impactful contributor to next-generation autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Online Dynamic Motion Planning and Control for Wheeled Biped Robots50 citations · 2020
- 2
- 3Robust Footstep Planning and LQR Control for Dynamic Quadrupedal Locomotion39 citations · 2021
- 4
- 5Straight leg walking strategy for torque-controlled humanoid robots14 citations · 2016
- 6
- 7
- 8Neural-Network-Controlled Spring Mass Template for Humanoid Running8 citations · 2018
- 9
- 10