Yangyang Zhao
Papers
1
Total Citations
11
H-Index
1
About
Yangyang Zhao is a researcher at the forefront of intelligent robotics and autonomous manipulation, with a focus on integrating computer vision and reinforcement learning for real-world grasping tasks. Their most cited work, "An Object Recognition Grasping Approach Using Proximal Policy Optimization With YOLOv5" (2023, 11 citations), addresses critical limitations in traditional mobile manipulator grasping—namely, narrow application scenarios, low accuracy, and task complexity. Zhao’s key contribution lies in synergizing YOLOv5’s real-time object detection with Proximal Policy Optimization (PPO) to enable adaptive, vision-guided grasping in dynamic environments. This hybrid approach not only improves precision but also enhances the robot’s ability to generalize across diverse objects and settings. By bridging deep learning and reinforcement learning, Zhao’s work advances the practicality of autonomous mobile manipulation, offering a scalable solution for industrial and service robotics. Their research is particularly impactful for students and engineers seeking to combine perception and control in robotic systems. With a growing citation footprint, Yangyang Zhao is establishing a reputation for innovative, application-driven contributions that push the boundaries of intelligent grasping.
Research Focus
Key Achievements
Top Papers
- 1