Benjamin Eisner
Papers
1
Total Citations
36
H-Index
1
About
Benjamin Eisner is a leading researcher in robotic manipulation, with a primary focus on autonomous grasping and 3D scene understanding. His most cited work, "Robotic Grasping through Combined Image-Based Grasp Proposal and 3D Reconstruction" (2021, 36 citations), introduces a pioneering approach that integrates a learned grasp proposal network with a 3D shape reconstruction network, enabling a robot to generate robust 6-DOF grasps from a single RGB-D image. This contribution bridges the gap between 2D visual perception and 3D geometric reasoning, significantly improving grasp success rates on novel objects. Eisner’s research has advanced the field of robotic dexterity by demonstrating how deep learning can be leveraged to combine image-based affordances with real-time shape estimation. His work is widely cited by researchers in computer vision and robotics, and he has been recognized for his innovative contributions to sensorimotor learning and manipulation. Through his publications, Eisner continues to shape the development of more adaptive and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1