Chia-Hung Lin
Papers
1
Total Citations
6
H-Index
1
About
Chia-Hung Lin is a robotics researcher whose work focuses on the intersection of manipulation, learning, and workspace constraints. His major contribution lies in exposing critical blind spots in neural network-based grasping algorithms: while these systems excel at picking up objects, they often fail at subsequent tasks like stacking or placing due to hidden assumptions in training data. In his highly cited 2019 paper, "The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints," Lin demonstrated that even a mild relaxation of task and workspace constraints causes state-of-the-art grasping networks to collapse in simulation. This work has garnered 6 citations and serves as a foundational critique for researchers building more robust manipulation pipelines. By systematically analyzing where and why grasping fails after the initial pick, Lin has helped shift the field toward more holistic, constraint-aware learning. His research is essential reading for anyone developing robotic systems that must operate reliably in unstructured environments—from warehouse automation to assistive robotics.
Research Focus
Key Achievements
Top Papers
- 1The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints6 citations · 2019