Papers
6
Total Citations
42
H-Index
3
About
Roger Sarango’s research lies at the intersection of deep reinforcement learning, industrial robotics, and assistive technology, with a strong emphasis on real-world automation and social impact. His most cited work introduces a deep reinforcement learning framework for controlling robotic manipulators in simulated environments (26 citations), addressing the need for robust, adaptive control in complex industrial settings. He has also advanced fractional-order PID control for robotic prostheses as part of the “Hand of Hope” project, aiming to deliver low-cost upper-limb solutions for individuals with motor disabilities. Sarango’s applied contributions include an integrated system combining industrial robotics and machine vision to automate hinge assembly and packaging, as well as motion control algorithms for 3D concrete printing in low-cost housing. He has further demonstrated the use of convolutional neural networks with robotic arms for automatic coffee bean selection. With a portfolio spanning simulation-based learning, embedded control, and vision-guided automation, Sarango’s work is notable for bridging cutting-edge AI techniques with tangible engineering solutions that improve productivity and accessibility.
Research Focus
Key Achievements
Top Papers
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