Papers
2
Total Citations
13
H-Index
2
About
Wenpeng Ma is an emerging researcher specializing in robotics, autonomous systems, and reinforcement learning, with a particular focus on advancing intelligent manipulation and navigation capabilities for mobile robotic platforms. His work sits at the intersection of computer vision, deep learning, and robot control, tackling real-world challenges that traditional methods have struggled to address effectively. Ma's most recognized contribution, "An Object Recognition Grasping Approach Using Proximal Policy Optimization With YOLOv5" (2023), has garnered 11 citations and demonstrates his innovative approach to combining state-of-the-art object detection with reinforcement learning. By integrating the YOLOv5 detection framework with PPO-based decision-making, Ma addresses critical limitations of conventional grasping methods, including poor adaptability, low accuracy, and restricted application scenarios. His complementary work on path planning fuses the Deep Deterministic Policy Gradient algorithm with Artificial Potential Field principles, improving convergence speed and learning efficiency for autonomous navigation and obstacle avoidance. Though early in his career, Ma's research reflects a sophisticated understanding of how hybrid AI approaches can elevate robotic performance in complex environments. His contributions offer promising foundations for students and practitioners working on next-generation autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2