Ben Money-Coomes
Papers
2
Total Citations
91
H-Index
2
About
Ben Money-Coomes is a leading researcher in dexterous robotics and tactile sensing, whose work focuses on making high-resolution robot touch accessible and affordable. His primary contributions center on developing and comparing low-cost tactile sensors that enable deep learning-driven manipulation. Money-Coomes is best known for his work on the DIGIT tactile sensor, a low-cost, high-resolution GelSight-type sensor that has garnered 86 citations and is widely recognized for lowering the barrier to entry in tactile robotics. He also pioneered the DigiTac sensor, a hybrid of DIGIT and TacTip designs, which provides a direct comparison between different sensing approaches and has already attracted 5 citations since its 2022 publication. By systematically evaluating these sensors, Money-Coomes has helped the robotics community understand trade-offs in resolution, cost, and durability, accelerating progress toward highly capable dexterous hands. His research is notable for its practical focus, enabling labs with limited budgets to explore tactile deep learning. Money-Coomes’ work is essential reading for any student or researcher aiming to integrate touch into robotic systems, as it provides both the hardware and the experimental frameworks needed to advance the field.
Research Focus
Key Achievements
Top Papers
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