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About

Shida Xu is a robotics researcher whose work spans mobile manipulation, perception-aware navigation, and 3D LiDAR representation. Their major contributions lie at the intersection of reinforcement learning and robotic control, particularly for dynamic environments. In their most-cited work (8 citations), Xu developed a multi-task reinforcement learning framework for mobile manipulators to track and grasp moving objects—a notoriously difficult problem due to the coupled complexity of robotic systems and unstructured environments. Their adaptive heading approach (5 citations) improves localization accuracy during trajectory following by dynamically adjusting a robot’s orientation to enhance feature tracking, a key advance for autonomous navigation. Xu also contributed to LiDAR data compression with CURL (5 citations), a continuous, ultra-compact representation that addresses the cost and storage challenges of high-density point clouds. Additional work on autonomous underwater grasping (2 citations) demonstrates their versatility, proposing a modular, hierarchical framework for underwater vehicle manipulator systems. Xu’s research is notable for tackling real-world robotic challenges—from dynamic object interaction to perception-aware autonomy—with practical solutions that advance both ground and underwater robotics.