Benno Staub

Papers

Total Citations

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About

Benno Staub is a robotics researcher whose work focuses on the intersection of mobile manipulation, deep learning, and autonomous grasping in unstructured environments. His most-cited paper, "Dex-Net MM: Deep Grasping for Surface Decluttering with a Low-Precision Mobile Manipulator" (2019, 10 citations), addresses a critical challenge in service robotics: enabling mobile robots to perform surface decluttering tasks in real-world settings like homes and machine shops. Unlike traditional fixed industrial manipulators, mobile robots suffer from low-precision sensors and actuators, making reliable object recognition and grasping difficult. Staub's major contribution lies in adapting deep learning-based grasping algorithms—specifically the Dex-Net framework—to work effectively with these hardware limitations, demonstrating that mobile manipulators can autonomously recognize, grasp, and sort objects into bins with practical accuracy. This work has implications for assistive robotics, warehouse automation, and domestic service robots. While his citation count is modest, the research represents a meaningful step toward bridging the gap between lab-based robotic manipulation and real-world deployment. Staub's approach highlights the importance of algorithm robustness in the face of sensor noise and mechanical imprecision, a key challenge for the next generation of mobile robots.