Papers
3
Total Citations
17
H-Index
2
About
Jibo Bai is a researcher at the forefront of intelligent prosthetic control and robotic manipulation, specializing in the intersection of bionics, reinforcement learning, and human-robot interaction. His work focuses on developing adaptive control systems that enable prosthetic hands and dexterous robotic grippers to perform complex, human-like movements. Bai’s major contributions include pioneering a bionic hand motion control method that mimics natural human hand gestures while leveraging reinforcement learning for real-time adaptation, a breakthrough that has garnered 11 citations since its 2024 publication. He has also advanced the field of shoulder-disarticulation prosthetics, designing interactive control algorithms that help amputees regain functional arm movement by learning from object interactions—a critical step toward restoring autonomy for individuals with high-level limb loss. Additionally, Bai’s research on grasp-with-push policies for multi-finger hands demonstrates how deep reinforcement learning can enable robots to manipulate objects more dexterously, even in cluttered environments. With a growing citation record and a clear focus on translating AI-driven control into tangible assistive technologies, Bai is establishing himself as a key contributor to next-generation prosthetic and robotic systems.
Research Focus
Key Achievements
Top Papers
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