Jie-Xin Liu
Papers
1
Total Citations
11
H-Index
1
About
Jie-Xin Liu is a leading researcher in human-robot interaction and embodied AI, with a focus on enabling seamless, safe physical collaboration between humans and machines. His core contributions lie in visual grasping, federated learning for robotics, and human-robot handover systems—critical for service robots in healthcare, logistics, and manufacturing. Liu’s most cited work, "Fed-HANet: Federated Visual Grasping Learning for Human Robot Handovers" (2023, 11 citations), pioneers a federated learning framework that allows robots to learn robust, object-agnostic grasping policies across distributed environments without sharing sensitive data. This addresses a key bottleneck in deploying robots that must adapt to diverse, real-world human interactions. By integrating six-degree-of-freedom (DoF) grasping with privacy-preserving collaborative training, Liu’s research bridges the gap between simulation and practical deployment, significantly improving robot dexterity and safety during object handovers. His work has been recognized for advancing the reliability of autonomous systems in human-centric settings, with growing citation impact that underscores its relevance to both academia and industry. Liu’s contributions are shaping the next generation of socially aware, adaptive robots capable of working alongside people in dynamic, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1Fed-HANet: Federated Visual Grasping Learning for Human Robot Handovers11 citations · 2023