Xiaokuan Fu
Papers
1
Total Citations
19
H-Index
1
About
Xiaokuan Fu is a leading researcher in intelligent robotic manipulation, with a focus on vision-based grasping and reinforcement learning. His most cited work, "Ensemble Bootstrapped Deep Deterministic Policy Gradient for Vision-Based Robotic Grasping" (2021), tackles a fundamental challenge in robotics: enabling manipulators to grasp novel objects without prior knowledge, mirroring human dexterity. By integrating ensemble bootstrapping with deep deterministic policy gradients, Fu’s approach significantly improves sample efficiency and generalization in robotic grasping tasks, moving beyond traditional algorithms that rely on pre-defined object models. This work has garnered 19 citations and is foundational for developing adaptive industrial automation systems. Fu’s contributions bridge the gap between human-like decision-making and robotic control, with implications for manufacturing, logistics, and assistive robotics. His research continues to push the boundaries of autonomous manipulation, making him a notable figure in the intersection of computer vision and reinforcement learning.
Research Focus
Key Achievements
Top Papers
- 1