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About

Jingxi Xu is a robotics researcher whose work spans multi-arm motion planning, tactile sensing, assistive robotics, and robot learning. With expertise bridging perception, control, and machine learning, Xu has made notable contributions to some of the most challenging problems in modern robotics. Among Xu's most recognized contributions is a decentralized multi-arm motion planner (2020, 19 citations) that overcomes the exponential runtime limitations of traditional centralized systems, enabling scalable coordination in dynamic environments. Complementing this, Xu has advanced dynamic grasping strategies that incorporate reachability and real-time motion prediction for moving targets. A significant thread of Xu's research focuses on assistive robotics for stroke rehabilitation, including the development of robotic hand orthoses featuring thumb stabilization mechanisms, adaptive EMG-based intent inferral, and innovative data solutions such as meta-learning (MetaEMG) and synthetic data generation (ChatEMG) to address the scarcity of labeled training data from disabled-bodied subjects. Xu has also pioneered tactile sensing research, exploring vibration-based texture classification, contact estimation, and vision-free manipulation through the TANDEM framework. Together, these contributions demonstrate a researcher deeply committed to making robots more adaptive, accessible, and responsive to real-world human needs.