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About

Haocun Wu is a robotics researcher whose work centers on motion planning, trajectory imitation learning, and autonomous grasping. Their key contributions lie in advancing Dynamic Movement Primitives (DMPs) for robotic manipulators, particularly in solving obstacle avoidance and multi-trajectory learning challenges. Wu’s most-cited paper (2022, 31 citations) introduces a motion planning method that integrates DMPs with a modified obstacle-avoiding algorithm, significantly improving trajectory performance in task space. Building on this, their 2024 work (7 citations) extends DMPs into a probabilistic framework combined with model predictive planning, offering greater flexibility for enhanced imitation learning—a notable step beyond single-trajectory constraints. Earlier, Wu developed an RGB-D-based object recognition and grasping system (2020, 6 citations), enabling robots to autonomously segment point cloud data and grasp objects from a custom dataset. This foundational work demonstrates a practical pipeline from perception to action. With a growing citation footprint, Wu’s research bridges classical control and modern learning methods, making impactful strides toward more adaptive and intelligent robotic systems.