Ruibo Cui

Papers

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About

Ruibo Cui is a researcher in robotics and autonomous systems, with a primary focus on motion planning and humanoid robot manipulation. Their most cited work, "Target Grasping and Obstacle Avoidance Motion Planning of Humanoid Robot" (2018, 2 citations), addresses a critical challenge in service robotics: enabling robots to safely navigate and grasp objects in cluttered environments. Cui's key contribution lies in developing obstacle avoidance planning methods for robotic arms within the Robot Operating System (ROS) platform, leveraging the Unified Robot Description Format (URDF) to model and control humanoid robots. This work is foundational for advancing service robots that can operate autonomously in dynamic, real-world settings. Though early in their career, Cui's research bridges theoretical motion planning with practical implementation, offering a framework for integrating perception and action in humanoid systems. Their efforts contribute to the broader goal of creating robots capable of assisting humans in daily tasks, from household chores to industrial applications. As the field evolves, Cui's work on ROS-based obstacle avoidance remains a stepping stone for more sophisticated, adaptive robotic behaviors.