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About

Tianqi Ma is a researcher at the intersection of robotics, control theory, and bio-inspired motion, whose work explores how intelligent systems can learn and adapt to complex physical challenges. His primary research areas include deep reinforcement learning (DRL) for robot control, cable-driven parallel robots, and bio-inspired locomotion. Ma’s most significant contribution is his pioneering application of DRL to cable-driven parallel robots, demonstrating that end-to-end and hybrid DRL strategies can outperform traditional control methods—a finding published in his 2019 paper, which has garnered 51 citations and established a foundation for adaptive control in underactuated systems. He also authored a related study on DRL-based control of these robots, contributing 10 citations. Beyond robotics, Ma’s work on the dynamics and control of a squirrel locking its head during a tumbling fall (5 citations) showcases his talent for extracting engineering principles from nature, offering insights for stabilization in aerial robotics. Additionally, his research on fine-grained pitching action recognition using ConvGRU networks (4 citations) extends his expertise into human-robot collaboration and video understanding. With a growing citation record and a portfolio that bridges theory, simulation, and biological inspiration, Tianqi Ma is a rising voice in autonomous systems and adaptive control.