Qianyuan Liu

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About

Qianyuan Liu is a robotics researcher whose work spans collaborative manipulation, aerial robotics, and humanoid perception. His primary research areas include dual-arm robot coordination, aerial manipulator trajectory planning, and real-time object recognition for service robots. Liu's most impactful contribution is a deep reinforcement learning-based collaborative control method for dual-arm robots, which addresses the critical challenge of collision avoidance and cooperative task execution—a paper that has garnered 36 citations and represents a significant advance in multi-arm robotic systems. He has also tackled the complex problem of online trajectory generation for unmanned aerial manipulators (UAMs), developing methods to satisfy multiple constraints and tasks simultaneously for these kinematically redundant systems. Additionally, his work on real-time object recognition using NAO humanoid robots contributes to improving the perceptual capabilities of indoor service robots. Liu's research demonstrates a consistent focus on enabling robots to operate safely and effectively in dynamic, constrained environments, bridging the gap between theoretical control methods and practical robotic applications.