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About

Qiyan Tian is a robotics researcher whose work spans mobile manipulation, underwater vision, and bio-inspired robotic systems. Her most impactful contribution, "Learning Mobile Manipulation through Deep Reinforcement Learning" (96 citations), addresses the complex coordination between a mobile base and a manipulator—a challenge far more demanding than fixed-base manipulation. This work has become a key reference for researchers applying deep reinforcement learning to real-world robotic tasks. Tian has also made notable advances in underwater robotics, including a stereo vision system for object detection and 3D reconstruction (16 citations), a novel thrust allocation method for improved maneuverability in position-keeping tasks, and an energy-storage buoyancy regulating system for low-energy vertical movement. Her bio-inspired work includes gait planning for a crablike robot with leg-paddle hybrid propulsion. Beyond the lab, Tian contributed to the high-profile deployment of robots at the Beijing 2022 Winter Olympics, demonstrating how robots can serve, protect, and assist in large-scale events. Her research consistently bridges theoretical innovation and practical application, making her a rising figure in both terrestrial and underwater robotics.