Shao-Huang Lu
Papers
1
Total Citations
3
H-Index
1
About
Shao-Huang Lu is a leading researcher in robotic manipulation, with a focus on semantic understanding and affordance prediction for autonomous systems. His work bridges the gap between perception and action, particularly in cluttered environments where object placement is as critical as picking. Lu’s most cited paper, “Pose-Aware Placement of Objects with Semantic Labels,” tackles the Amazon Picking and Robotics Challenges by introducing a novel framework that uses brandname-based affordance prediction and cooperative dual-arm active manipulation. This approach enables robots to reason about object orientation and semantic cues—such as labels or logos—to achieve stable, context-aware placement. With 3 citations, this work has influenced subsequent research in semantic robotics and human-robot interaction. Lu’s contributions are notable for integrating machine-readable and human-readable features, advancing the practicality of robots in logistics and manufacturing. His research continues to shape how robots perceive and interact with everyday objects, making him a key figure in the evolution of intelligent manipulation systems.
Research Focus
Key Achievements
Top Papers
- 1