About

Xia Jing is a leading researcher in robotics, with a focus on human-robot interaction, assistive robotics, and manipulation. Her work spans key areas including grasp pose detection, compliant actuation, and human-like motion planning. A major contribution is her development of affordance-based task constraint learning for grasp pose detection in single-view point clouds, a method that enhances robotic dexterity in unstructured environments. She has also advanced series elastic actuators (SEAs) for assistive robots, notably designing a clutchable SEA for a robotic hip exoskeleton, which improves torque precision and safety in physical human-robot interactions. Her dual fast marching tree algorithm enables human-like motion planning for anthropomorphic arms under task constraints, while her framework for S-R-S-redundant manipulators simultaneously addresses trajectory tracking, obstacle avoidance, and human-like movement. With over 140 citations across her top papers, Jing’s work is widely recognized for its impact on safe, compliant, and intuitive robotic systems. Her achievements include pioneering hybrid safety-control strategies and real-time collision detection methods, solidifying her reputation as an innovator in making robots safer and more effective for collaborative environments.

Research Focus

Key Achievements

6
H-Index
11
Papers
149
Total Citations
14
Avg Citations/Paper
🏆 Most Cited Paper
Grasp Pose Detection with Affordance-based Task Constraint Learning in Single-view Point Clouds
33 citations · 2020
📈 Most Prolific Year: 2019 (3 Papers)
🤝 Key Collaborators: 26
🏛 Institutions: Ministry of Education of the People's Republic of China, Xi'an University of Science and Technology, Harbin Institute of Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago