Juxing Wang

Ningbo Institute of Industrial Technology

Papers

Total Citations

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About

Juxing Wang is a researcher in robotics and human-robot interaction, with a focus on motion prediction and imitation learning. Their work addresses critical challenges in enabling robots to work safely and efficiently alongside humans. In their highly cited study on human motion prediction, Wang developed a novel approach that combines neural network-based initial trajectory prediction with motion optimization, significantly improving the accuracy of long-term motion forecasts—a key requirement for responsive human-robot cooperation. This paper has garnered 4 citations, reflecting its relevance to advancing autonomous systems. Wang has also made notable contributions to robotic assembly through imitation learning. Their research on peg-in-hole assembly introduced Kernelized Movement Primitives to address the limitations of traditional Dynamical Movement Primitives, enabling robots to better handle uncertainty from multiple demonstration trajectories. This work, with 3 citations, demonstrates Wang’s ability to refine machine learning techniques for practical manipulation tasks. By bridging neural networks, optimization, and skill acquisition, Wang’s research continues to push the boundaries of how robots learn from and adapt to human behavior in real-world settings.