Max Robotics — Global Service Robot Directory + US Certification

About

Carlos Rubert’s research lies at the intersection of robotic grasping, prosthetic hand design, and grasp quality assessment. His major contributions include developing and characterizing grasp quality metrics—analytical tools that quantify how well a robotic or prosthetic hand can hold an object. His work on comparing tendon and linkage transmission systems in low-cost, 3D-printed hand prostheses has been particularly influential, with 18 citations, highlighting the potential of open-source, DIY approaches to assistive technology. Rubert’s studies, such as his 2017 paper on grasp quality metrics (29 citations) and his 2014 work on benchmarking robot hands (7 citations), provide foundational frameworks for evaluating prehension capabilities. He also contributed to practical applications, including the UJI RobInLab’s approach to the Amazon Robotics Challenge 2017, which tackled pick-and-place operations in unstructured environments. By bridging theoretical metrics with real-world validation—such as predicting grasp success through human assessment—Rubert has advanced both the science and engineering of robotic manipulation, offering insights that improve prosthetic hand design and autonomous grasping systems.