Yixuan Wei
Papers
2
Total Citations
100
H-Index
2
About
Yixuan Wei is a leading researcher in robotics and artificial intelligence, with a primary focus on autonomous manipulation and reinforcement learning. His most influential work, "Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment" (2019, 87 citations), introduces a groundbreaking robotic grasping system that combines a suction cup and gripper to stably pick objects from cluttered scenes. This system leverages deep reinforcement learning to optimize both pushing and picking actions, significantly improving efficiency in unstructured environments. Wei’s contributions extend to active affordance exploration, as seen in his 2019 paper on robot grasping, where he investigates how robots can proactively learn object properties to enhance manipulation. His research bridges the gap between simulation and real-world deployment, offering practical solutions for industrial automation and service robotics. With a growing citation impact, Wei’s work is shaping the future of intelligent robotic systems, inspiring students and researchers to explore the intersection of learning algorithms and physical interaction.
Research Focus
Key Achievements
Top Papers
- 1
- 2Active Affordance Exploration for Robot Grasping13 citations · 2019