Yulin Xu
Papers
8
Total Citations
64
H-Index
4
About
Yulin Xu is a robotics researcher whose work centers on robotic grasping, manipulation, and human-robot interaction, with a particular focus on integrating computer vision and bionic design. Xu’s most impactful contribution is the development of GraspCNN (2019, 32 citations), a novel approach that simplifies robotic grasp detection by representing feasible grasps as oriented diameter circles in RGB images, enabling real-time performance with a single convolutional neural network. This work addresses a fundamental challenge in robotics: enabling robots to quickly and accurately identify how to pick up objects. Xu has also made significant strides in bionic hand design, including the SHU-hand II humanoid robotic hand (2018) and compliance control methods for grasping with under-actuated fingers (2016, 7 citations), which analyze how spring stiffness and object properties affect dynamic response. Further contributions include collaborative robot systems using gaze interaction (2021), Kinect-based human body tracking for medical service robots (2018), and multi-object dynamic grasping using convolutional neural networks (2020). With a total of over 60 citations across these works, Xu’s research bridges perception, control, and mechanical design, advancing the practical deployment of robots in healthcare, manufacturing, and service environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Survey of 3D Map in SLAM: Localization and Navigation9 citations · 2017
- 3Compliance control for grasping with a bionic robot hand7 citations · 2016
- 4Collaborative Robot Grasping System Based on Gaze Interaction5 citations · 2021
- 5
- 6Real-Time Multi-object Grasp Based on Convolutional Neural Network3 citations · 2020
- 7Motion Planning and Object Grasping of Baxter Robot with Bionic Hand3 citations · 2017
- 8