Yanzhe Wang
Papers
1
Total Citations
2
H-Index
1
About
Yanzhe Wang is a robotics researcher whose work focuses on advancing dexterous manipulation for non-graspable objects—a critical challenge in real-world robotic interaction. His key research areas include reinforcement learning, robotic manipulation, and stiffness-aware control systems. Wang’s major contribution is the development of SA-DEM (Stiffness-Aware Dual-Stage Extrinsic Manipulation), a novel framework that enables robots to manipulate objects that cannot be conventionally grasped, such as thin, slippery, or irregularly shaped items. By decoupling the manipulation task into two sequential phases—interactive mode decision-making and manipulation action planning—his approach achieves precise extrinsic control through environmental contact rather than direct grasping. This dual-stage reinforcement learning method represents a significant advance in robotic dexterity, expanding the range of objects robots can handle in unstructured settings. Though his most-cited paper has garnered early attention with 2 citations since its 2025 publication, the work’s innovative framework positions it as a foundational contribution to the emerging field of non-grasping manipulation. Wang’s research holds promise for applications in manufacturing, healthcare, and assistive robotics, where handling ungraspable objects is essential.
Research Focus
Key Achievements
Top Papers
- 1