Ben Cui

About

Ben Cui is a leading researcher in intelligent robotics, specializing in the integration of deep reinforcement learning with robotic manipulation and control systems. His work focuses on solving critical challenges in robot grasping, visual servoing, and precision assembly tasks. Cui's most impactful contribution is his 2021 paper on optimizing robot grasping using an improved deep deterministic policy gradient algorithm, which has garnered 26 citations and addresses the inefficiencies of traditional object detection methods. He further advanced the field with his 2022 study on adaptive visual servoing for 6-degree-of-freedom robotic manipulators, introducing a novel DQN-PID controller that enables real-time tracking of moving targets—a paper cited 13 times. His research on deep reinforcement learning for peg-in-hole assembly tasks, utilizing innovative information utilization methods, demonstrates his commitment to solving complex industrial automation problems. With a total of 47 citations across his top works, Cui's contributions are shaping the future of autonomous robotic systems, making him a notable figure in the intersection of reinforcement learning and robotics engineering.

Research Focus

Key Achievements

Top Papers

1. 1
   Robot grasping method optimization using improved deep deterministic policy gradient algorithm of deep reinforcement learning

   26 citations · 2021
2. 2
   Tracking moving target for 6 degree-of-freedom robot manipulator with adaptive visual servoing based on deep reinforcement learning PID controller

   13 citations · 2022
3. 3
   Deep Reinforcement Learning for Peg-in-hole Assembly Task Via Information Utilization Method

   8 citations · 2022

Key Collaborators

Contact & Links