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About

Shuxiao Chen is an emerging robotics researcher whose work sits at the intersection of legged locomotion, autonomous navigation, and control theory. His research focuses on enabling bipedal and quadrupedal robots to move intelligently and robustly through complex, real-world environments — a challenge that demands tight integration of perception, planning, and control. Among his most influential contributions is his work on torque-based reinforcement learning for quadrupedal locomotion (2023, 39 citations), which challenges the conventional position-control paradigm and demonstrates that directly learning torque outputs can yield more natural and adaptive robot movement. His vision-aided discrete terrain traversal framework (2022, 25 citations) further advances quadrupedal agility by combining motion libraries with visual feedback to handle unpredictable footholds. Chen has also made notable strides in bipedal robotics, developing autonomous navigation systems for height-constrained environments (2023, 29 citations) that allow large-scale robots to crouch and maneuver through cluttered spaces. His creative work enabling the Cassie bipedal robot to autonomously ride Hovershoes (2019, 14 citations) showcases his interest in multi-modal locomotion. With over 110 cumulative citations across recent publications, Chen is establishing himself as a distinctive voice in next-generation legged robot autonomy.