Xingxuan Zhang
Papers
2
Total Citations
31
H-Index
2
About
Xingxuan Zhang is a rising researcher at the intersection of robotics, human motion analysis, and continual machine learning. His work centers on enabling robots and exoskeletons to perceive, predict, and adapt to human intent and dynamic environments. A major contribution is the development of **MoFCNet**, a motion forecasting network that uses IMU data to predict human motion intention for hip assistive exoskeletons—a critical step toward responsive, safe wearable robotics. This work has garnered 19 citations since 2023, reflecting its immediate relevance to rehabilitation and human augmentation. Zhang also pioneered the **Class Incremental Robotic Pick-and-Place (CIRPAP)** task, which challenges robots to learn to handle new object categories without forgetting previously learned skills. This research, cited 12 times, addresses the fundamental problem of catastrophic forgetting in few-shot scenarios, pushing the boundaries of lifelong learning in robotic manipulation. By bridging sensor-based motion forecasting with incremental object detection, Zhang is shaping a future where robots and exoskeletons can continuously adapt to new tasks and users—a vision that promises to make assistive and industrial robotics more intelligent, flexible, and human-centered.
Research Focus
Key Achievements
Top Papers
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