Papers
11
Total Citations
224
H-Index
7
About
Jianjie Lin is a robotics researcher whose work spans robotic grasping detection, motion planning, point cloud processing, and adaptive robot control. His most influential contribution, "Efficient Grasp Detection Network With Gaussian-Based Grasp Representation for Robotic Manipulation" (2022, 68 citations), introduced a novel deep learning framework that strikes a practical balance between accuracy and real-time inference speed — a persistent challenge in robotic manipulation. Complementing this, his series of lightweight and convolutional network architectures for grasping detection (2021, accumulating over 45 citations collectively) demonstrates a sustained effort to make robotic perception both computationally efficient and deployment-ready. Beyond perception, Lin has made notable contributions to trajectory planning, proposing time-optimal, jerk-limited approaches for robot manipulators navigating complex kinematic constraints, with his 2018 work earning 34 citations. His 2021 PCTMA-Net architecture advanced 3D point cloud completion using transformer-based methods, addressing critical limitations in structural detail recovery. Further breadth is shown through research in adaptive inverse dynamics control under uncertainty and oscillation damping for pendulum-like platforms. More recently, Lin has explored cloud-native fog robotics for real-time deployment. With a growing citation profile exceeding 220 citations, his research consistently bridges theoretical rigor with practical robotic applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10