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About

Jiajun Xie is a leading researcher in intelligent robotics, specializing in humanoid robot control, motion planning, and deep reinforcement learning. His work addresses critical challenges in robot stability and precision during dynamic locomotion. Xie’s most cited paper (2020, 13 citations) introduces a deep reinforcement learning-based attitude motion controller that integrates stability constraints, significantly improving balance and tracking accuracy. This work demonstrates a 60.97% reduction in torso pitch trajectory tracking error compared to traditional PID controllers. He also pioneered the use of Adaboost neural networks for inverse kinematics of 6-DOF offset-wrist robots (2017, 7 citations), enhancing computational efficiency for complex manipulator control. In his 2020 study on intelligent posture control in variable environments, Xie developed a novel training framework that uses robot identification models for offline pre-training, overcoming the challenge of limited physical samples. This approach enables faster, more robust learning in real-world settings. With a focus on bridging simulation and reality, Xie’s contributions are pivotal for advancing autonomous, stable humanoid robots capable of operating in unstructured environments.