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About

Mingdi Deng is a leading researcher in the field of robotic exoskeletons and human-robot interaction, with a focus on developing intelligent control systems that enable seamless cooperation between humans and machines. Their work centers on admittance control, hierarchical control schemes, and reinforcement learning to enhance the physical capabilities of assistive and mobile robots. Deng’s most influential paper, “Physical Human–Robot Interaction of a Robotic Exoskeleton By Admittance Control” (2018), has garnered 296 citations for its innovative approach to interpreting human intention and managing unknown dynamics in exoskeleton systems. Another highly cited work, “A Learning-Based Hierarchical Control Scheme for an Exoskeleton Robot in Human–Robot Cooperative Manipulation” (2018, 109 citations), introduces a two-layer control architecture that significantly improves cooperative manipulation. Deng has also advanced mobile dual-arm robotics through the use of dynamic movement primitives and reinforcement learning, as seen in their 2018 and 2017 papers. These contributions have positioned Deng as a key figure in making robotic systems more adaptive, intuitive, and effective for real-world applications, from rehabilitation to daily living assistance.