Papers
3
Total Citations
101
H-Index
2
About
Di Gan is a leading researcher in the field of rehabilitation and assistive robotics, with a core focus on developing intelligent lower-limb exoskeletons for human locomotion. His work centers on integrating advanced control strategies—such as reinforcement learning and adaptive fuzzy control—with biomechanical design to create systems that can both assist walking and support rehabilitation. Gan’s most impactful contribution is his 2019 paper on using Dynamic Movement Primitives (DMPs) combined with reinforcement learning for motion generation in walking exoskeletons, which has garnered 85 citations. This work pioneered a novel method for coupled movement planning and real-time adaptation, enabling an 8-DOF exoskeleton to learn and generalize walking patterns. He also made foundational contributions to device design, including a 4-DOF lower-limb rehabilitation robot and a 6-DOF exoskeleton with passive body-weight support, both detailed in his earlier publications. Through these efforts, Gan has established himself as a key figure in advancing human-robot interaction for mobility assistance, with his research bridging the gap between theoretical control algorithms and practical, wearable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Development of a exoskeleton robot for lower limb rehabilitation14 citations · 2016
- 3