Xuechao Zhang
Papers
1
Total Citations
6
H-Index
1
About
Xuechao Zhang is a leading researcher in robotic manipulation, with a focus on enabling robots to grasp and interact with objects in cluttered, occluded environments. His key contributions lie at the intersection of affordance learning and active perception, most notably through his pioneering work on next-best-view planning for robotic grasping. In his highly cited 2023 paper, "Affordance-Driven Next-Best-View Planning for Robotic Grasping," Zhang introduced ACE-NBV, a policy that intelligently selects optimal viewpoints to reveal feasible grasps for target objects, even when they are partially hidden. This work, which has already garnered 6 citations, addresses a critical bottleneck in complex manipulation tasks by combining affordance reasoning with active camera control. Zhang’s research has significant implications for warehouse automation, domestic robotics, and industrial assembly, where robust grasping in unstructured settings is essential. His innovative approach to integrating perception and action continues to shape the field, offering practical solutions for real-world robotic dexterity.
Research Focus
Key Achievements
Top Papers
- 1Affordance-Driven Next-Best-View Planning for Robotic Grasping6 citations · 2023