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About

Wanhao Niu is a robotics researcher whose work focuses on advancing robotic manipulation in unstructured environments, with key contributions in 6-DoF and 7-DoF grasp pose generation, point cloud processing, and bin-picking. Niu’s most impactful work, "GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in Cluttered Scenes" (2023, 15 citations), introduces a graph-based approach that moves beyond traditional non-optimized sampling methods, learning task-specific spatial features to enable object-agnostic grasping in cluttered settings. This is complemented by a customizable 6-DoF grasping dataset and an interactive training method for graph convolutional networks (2024, 8 citations), which provides a valuable resource for the community. Niu also developed an Adaptive Multiview Graph Convolutional Network for 3-D point cloud classification and segmentation (2024, 7 citations), addressing critical tasks with applications in autonomous driving and robotics. Further contributions include sparse convolution-based 6D pose estimation for robotic bin-picking (2024, 4 citations) and a parallel graph network for 7-DoF model-free grasping (2024, 4 citations). With a growing citation record and a focus on graph-based learning for manipulation, Niu is establishing a notable presence in the field of robotic grasping and point cloud analysis.