Guilherme A. Barreto
Papers
15
Total Citations
265
H-Index
8
About
Guilherme A. Barreto is a leading researcher in computational intelligence and robotics, with a focus on self-organizing neural networks for temporal sequence learning and robot control. His major contributions include pioneering short-term memory mechanisms in neural classifiers for robot navigation, as demonstrated in his most-cited work (89 citations), which explores wall-following strategies. He developed innovative self-organizing feature maps for modeling and control of robotic manipulators (54 citations) and introduced context-based temporal sequence processing for robot trajectory planning (27 citations). Barreto’s work on unsupervised learning and recall of temporal sequences (13 citations) and distributed robotic control systems (13 citations) has advanced autonomous robotics. His notable achievements include kinesthetic teaching for humanoid robots like iCub (11 citations) and optimal PID-like controller tuning for minimum jerk trajectories (18 citations). With a total of over 250 citations across his top papers, Barreto’s research bridges neural network theory and practical robotics, impacting fields from prosthetics to satellite control. His work on spherical motors (11 citations) and resource-oriented middleware (6 citations) further underscores his versatility in designing intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 8Design and Control of a Three-Coil Permanent Magnet Spherical Motor11 citations · 2018
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