Lingguo Cui

About

Lingguo Cui is a researcher focused on advancing intelligent autonomous decision-making for mobile robots, with a particular emphasis on obstacle avoidance using deep reinforcement learning (DRL). His most-cited work, "An Obstacle Avoidance Method Using Asynchronous Policy-based Deep Reinforcement Learning with Discrete Action" (2022, 3 citations), addresses the growing demand for smarter navigation in manufacturing, service, and military applications. By applying state-of-the-art policy-based DRL algorithms, Cui’s research contributes to enabling robots to make real-time, adaptive decisions in complex environments, enhancing their autonomy and safety. While his citation count is still emerging, his work highlights a critical intersection of robotics and artificial intelligence, offering practical solutions for real-world deployment. Cui’s contributions are particularly notable for integrating asynchronous learning methods with discrete action spaces, a technical approach that improves efficiency and scalability in obstacle avoidance tasks. As mobile robots become increasingly integral to industry and daily life, Cui’s research lays foundational groundwork for more resilient and intelligent autonomous systems, marking him as a promising voice in the field of robotics and reinforcement learning.

Research Focus

Key Achievements

Top Papers

1. 1
   An Obstacle Avoidance Method Using Asynchronous Policy-based Deep Reinforcement Learning with Discrete Action

   3 citations · 2022

Key Collaborators

Contact & Links