Papers
1
Total Citations
2
H-Index
1
About
Zhu Bao is a robotics researcher whose work focuses on advancing robotic manipulation through deep learning, with a particular emphasis on grasp detection for parallel-plate grippers. His most-cited paper, "Robot Grasp Detection using Inverted Residual Convolutional Neural Network" (2022), introduces the IR-ConvNet model—an end-to-end architecture that processes RGB and depth images to predict single or multiple grasping poses. This modular system represents a significant contribution to the field, offering a computationally efficient approach to real-time robotic grasping. While his citation count is still emerging, with 2 citations on his top paper, Bao's work demonstrates a strong command of state-of-the-art convolutional neural network design, specifically leveraging inverted residual structures to balance accuracy and speed. His research sits at the intersection of computer vision and robotics, aiming to make autonomous grasping more reliable and accessible. As an early-career researcher, Bao is laying the groundwork for impactful contributions in robotic perception and manipulation, with potential applications in industrial automation and assistive robotics.
Research Focus
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Top Papers
- 1