Yibin Song
Papers
2
Total Citations
10
H-Index
2
About
Yibin Song is a researcher at the intersection of artificial intelligence, robotics, and biomechanics, with a focus on autonomous motion planning and control in dynamic and clinical environments. His work spans two key domains: intelligent agent behavior in simulated soccer leagues and precision control for orthopedic surgical robots. In his 2021 study on the Soccer Simulation 3D League, Song applied Q-learning algorithms to generate shooting behaviors during walking, demonstrating how reinforcement learning can enable adaptive, real-time decision-making in complex, multi-agent settings—a contribution that has garnered 6 citations and laid groundwork for embodied AI in sports robotics. More recently, Song has advanced the field of surgical robotics. His 2025 paper introduces a novel framework combining CDMP-based imitation learning with constrained optimization for motion planning and control of active robots in orthopedic surgery. This work directly addresses the critical challenge of reducing surgeon dependency during pedicle screw implantation, a procedure where precise pose alignment and drilling are paramount. By enabling robots to learn from expert demonstrations while respecting safety constraints, Song’s approach promises to enhance surgical autonomy and precision. With 4 citations already, this research marks a significant step toward safer, more reliable robot-assisted surgery. Song’s contributions are shaping the future of autonomous systems in both competitive and clinical arenas.
Research Focus
Key Achievements
Top Papers
- 1
- 2