Samuel Cavalcanti
Papers
1
Total Citations
6
H-Index
1
About
Samuel Cavalcanti is a researcher whose work sits at the intersection of robotics, machine learning, and computer vision. His primary focus is on developing intelligent control systems for robotic manipulators, with a particular emphasis on self-learning approaches. In his most cited work, "Self-learning in the inverse kinematics of robotic arm" (2017, 6 citations), Cavalcanti explores the use of Kohonen self-organizing maps—a classic unsupervised learning technique—combined with computer vision to enable a robotic arm to autonomously learn and refine its inverse kinematics. This contribution is notable for demonstrating how neural networks can replace traditional, hand-coded control algorithms, allowing robots to adapt their movements through experience rather than explicit programming. While his citation count reflects a focused, early-stage research impact, Cavalcanti’s work represents a meaningful step toward more autonomous and flexible robotic systems. His integration of self-learning mechanisms into kinematic control offers a foundation for future developments in adaptive robotics, particularly in applications where manual calibration is impractical.
Research Focus
Key Achievements
Top Papers
- 1Self-learning in the inverse kinematics of robotic arm6 citations · 2017