Matteo Bana

Papers

Total Citations

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About

Matteo Bana is a robotics researcher specializing in neuromorphic control systems for humanoid robots, with a particular focus on bipedal locomotion and rhythmic movement generation. His work centers on developing biologically inspired control architectures that enable robots to learn and autonomously reproduce complex periodic trajectories, drawing from models of central pattern generation (CPG) circuits found in biological nervous systems. Bana’s most significant contribution is his proposal of Chaotic Neural Networks (CNN) as an alternative to traditional CPG models for robotic applications, introducing a novel method for walking humanoid robots that leverages chaotic dynamics for movement generation and execution. His research has resulted in the development of a complete neuromorphic control system for a lightweight, 3D-printed humanoid biped robot, demonstrating how modular control architectures can learn and reproduce complex periodic behaviors. While his citation counts remain modest—with his most cited work, "A neuromorphic control architecture for a biped robot" (2019), accumulating 10 citations—Bana’s work represents an innovative intersection of computational neuroscience and robotics, offering a fresh perspective on how chaotic neural dynamics can be harnessed for robotic control. His approach stands as a promising alternative to conventional methods, contributing to the growing field of neuromorphic engineering for autonomous robotic systems.