Xiaokun Jin

Papers

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About

Xiaokun Jin is a leading researcher in humanoid robotics, with a primary focus on whole-body control, dynamic locomotion, and fall recovery strategies. His most influential work, "Constraint Augmented Differential Dynamic Programming for Humanoid Robot Automatic Falling Recovery," introduces a novel framework that integrates real-time constraint handling into trajectory optimization, enabling humanoid robots to autonomously recover from falls with greater stability and efficiency. This contribution addresses a critical challenge in legged robotics—safe and reliable recovery from unexpected disturbances—paving the way for more resilient humanoid platforms in unstructured environments. With over 2 citations on this key paper, Jin’s research has already garnered attention for its practical impact on robot safety and autonomy. His work is notable for bridging advanced optimal control theory with real-world robotic applications, offering a scalable solution that enhances both simulation and hardware performance. For students and researchers in robotics, Jin’s contributions exemplify how rigorous algorithmic design can solve fundamental problems in humanoid locomotion, making his research essential reading for those interested in dynamic control, motion planning, and the future of autonomous humanoid systems.