Longxin Chen
Papers
2
Total Citations
10
H-Index
2
About
Longxin Chen is a roboticist focused on advancing autonomous manipulation and multi-robot coordination in shared, unstructured environments. His research addresses two critical challenges: enabling robots to recover from unexpected failures during manipulation, and ensuring collision-free motion planning in crowded workspaces. In his seminal 2018 work, Chen developed a framework for grounded anomaly classification and recovery policies, allowing robots to autonomously detect and recover from external disturbances—a key step toward longer-term operational autonomy. This approach tackles the persistent failure modes that arise when models fail to capture real-world dynamics, earning 6 citations for its practical impact. Earlier, in 2016, Chen tackled the coordination problem in multi-robot workcells by proposing a sequence-modification method that resolves collisions and deadlocks without altering individual robot paths. By assuming pre-planned trajectories, his algorithm efficiently finds collision-free execution orders, a contribution cited 4 times for its relevance to industrial automation. Chen’s work bridges the gap between robust single-robot manipulation and scalable multi-robot coordination, making him a notable contributor to the future of human-robot collaboration in manufacturing and service robotics.
Research Focus
Key Achievements
Top Papers
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