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About

Zhangpeng Tu is a researcher advancing the frontiers of human-robot interaction and autonomous manipulation, with a focus on haptic shared control and deep reinforcement learning. His work centers on developing intelligent frameworks that seamlessly integrate human operators with robotic systems, particularly for tracking and grasping moving objects in dynamic environments. In his highly cited 2022 paper, Tu proposed a haptic shared control architecture that fuses human input with robotic autonomy, using Kalman filters for uncertainty estimation and artificial potential fields for obstacle avoidance—a contribution that has garnered 13 citations. He further extended this line of research with a deep reinforcement learning approach for flexible grasping of moving objects, enhancing robotic manipulability during autonomous tasks. Tu’s work also addresses the unique challenges of underwater robotics, as seen in his review of underwater robotic avatars and his unified shared control architecture for underwater vehicle–manipulator systems, which reduces operator cognitive load through task-priority coordination. His research is pivotal for applications ranging from manufacturing to deep-sea exploration, demonstrating a clear trajectory toward more capable, collaborative robotic systems.