Eduardo Paiva Okabe

Universidade Estadual de Campinas (UNICAMP)

Papers

Total Citations

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About

Eduardo Paiva Okabe is a researcher focused on the intersection of robotics, control systems, and advanced manufacturing. His work centers on the modeling, simulation, and control of robotic mechanisms, with a particular emphasis on SCARA (Selective Compliance Articulated Robot Arm) systems and deployable tensegrity structures. Okabe’s major contributions include developing a comprehensive model and simulation of a 3D printer based on a SCARA mechanism, which bridges additive manufacturing with robotic precision. He has also pioneered the use of reinforcement learning—specifically Proximal Policy Optimization, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient—to control a cable-driven SCARA robot, demonstrating how neural networks can enhance adaptability and performance in non-traditional robotic designs. Additionally, his work on form-finding methods for deployable tensegrity arms addresses complex inverse kinematics challenges, advancing the field of adaptive and reconfigurable robotics. Though his most-cited papers have garnered modest attention (with 3 and 2 citations each), they represent foundational steps in integrating learning-based control with novel mechanical architectures. Okabe’s research is notable for its forward-looking approach, combining simulation, control theory, and practical robotics to solve real-world engineering problems.