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About

Zhiheng Li is a versatile robotics and autonomous systems researcher whose work spans autonomous driving, space robotics, and continuum robot control. His most influential contribution, "Harmonious Lane Changing via Deep Reinforcement Learning" (2021, 118 citations), demonstrates his expertise in applying multi-agent deep reinforcement learning to enable safer, more cooperative autonomous vehicle behavior without reliance on Vehicle-to-Everything communication — a significant practical advance for real-world deployment. Early in his career, Li made notable strides in space robotics, developing structured light vision methods for determining the pose of large non-cooperative satellites and docking rings, addressing the critical challenge of on-orbit servicing where precise relative positioning is essential. More recently, his research has expanded into 3D object detection for autonomous driving, improving Bird's Eye View-based point cloud processing, and into continuum robot systems, where he has investigated external force sensing and pioneered behavior cloning-assisted reinforcement learning to overcome sparse reward challenges in cable-driven continuum space robots. Together, his body of work reflects a consistent drive to bridge advanced machine learning with complex real-world robotic systems, earning him growing recognition across multiple high-impact domains.