Papers
7
Total Citations
128
H-Index
6
About
Boyang Gao is a roboticist whose research sits at the intersection of teleoperation, computer vision, and intelligent grasping, with a clear focus on advancing industrial automation. His most influential work centers on enhancing bilateral teleoperation through vision-based virtual fixtures, a technique that uses stereo cameras to generate real-time guidance for remote-controlled robots, significantly improving precision and safety in complex manipulation tasks. This line of research, published in 2016, has garnered over 80 combined citations, establishing a foundation for safer human-robot collaboration. More recently, Gao has driven innovation in robotic grasping, developing deep learning architectures like the Double-Dot Network (DD-Net) for antipodal grasp detection and a cross-modal attention mechanism for RGB-D grasp detection. His 2023 work on depth-guided learning has already accumulated 20 citations, reflecting its impact on the field. Gao’s contributions are not limited to perception; he has also authored a comprehensive 2025 review on motion planning for contact tasks, underscoring his breadth. By bridging teleoperation and autonomous grasping, Gao is shaping the future of flexible, intelligent manufacturing systems.
Research Focus
Key Achievements
Top Papers
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- 4RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention20 citations · 2023
- 5Double-Dot Network for Antipodal Grasp Detection12 citations · 2021
- 6Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge10 citations · 2024
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