Lirui Wang
Papers
1
Total Citations
22
H-Index
1
About
Lirui Wang is a roboticist whose research centers on dexterous manipulation, learning-based control, and the intersection of perception and action in complex environments. His most cited work, "Hierarchical Policies for Cluttered-Scene Grasping With Latent Plans" (2022, 22 citations), tackles the longstanding challenge of 6D grasping in cluttered settings. Rather than relying on brittle open-loop pipelines or end-to-end methods that struggle with obstacles, Wang introduces a hierarchical framework that learns latent plans to guide grasping policies, enabling robust performance even when state estimation is imperfect. This contribution is notable for bridging the gap between high-level reasoning and low-level control, offering a scalable solution for real-world robotic manipulation. Wang’s work has been recognized for its practical impact, with applications in warehouse automation and assistive robotics. By focusing on latent representations and hierarchical structures, he provides a pathway for robots to operate reliably in unpredictable, obstacle-rich environments—a critical step toward autonomous systems that can handle the messiness of the physical world.
Research Focus
Key Achievements
Top Papers
- 1Hierarchical Policies for Cluttered-Scene Grasping With Latent Plans22 citations · 2022