Binglei Zhao
Papers
5
Total Citations
161
H-Index
4
About
Binglei Zhao is a rising researcher in robotics and artificial intelligence, specializing in robotic grasping and visual navigation in unstructured environments. Their work addresses the fundamental challenge of enabling robots to perceive and interact with cluttered, unseen spaces. Zhao’s major contributions include the development of REGNet, an end-to-end grasp detection network that processes single-view point clouds to generate reliable grasps for novel objects, a paper that has garnered 82 citations. They also introduced REGRAD, a large-scale relational grasp dataset that advances safe, object-specific grasping in clutter by incorporating object relationships, cited 43 times. In navigation, Zhao proposed a distance prediction method for Object Goal Navigation (ObjectNav), allowing agents to locate target objects in unfamiliar environments using semantically related cues, with 26 citations. This work bridges perception and action, enhancing robotic autonomy. Zhao’s research, with over 160 total citations, is pivotal for developing robots capable of operating in dynamic, real-world settings, making their contributions essential for students and researchers advancing robotic manipulation and embodied AI.
Research Focus
Key Achievements
Top Papers
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- 3Navigating to Objects in Unseen Environments by Distance Prediction26 citations · 2022
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