Chaitanva Nutakki

About

Chaitanva Nutakki is a researcher whose work lies at the intersection of robotics and machine learning, with a particular focus on intelligent motion control and classification systems. His most-cited paper, "Classification of robotic arm movement using SVM and Naïve Bayes classifiers" (2013), has garnered 10 citations and represents a foundational contribution to the field. In this work, Nutakki explored how target-oriented approaches in 3D space—specifically forward and inverse kinematics—can be enhanced through machine learning classifiers to predict robotic arm movements. By comparing Support Vector Machines (SVM) and Naïve Bayes algorithms, he demonstrated how prior learning can improve the accuracy and efficiency of robotic motion prediction, a critical step toward more autonomous and adaptive robotic systems. This research bridges the gap between classical robotics and modern AI, offering practical insights for developing smarter, more responsive robotic arms. Nutakki’s work is particularly valuable for students and researchers interested in the synergy between classification techniques and real-world robotic applications, highlighting the potential of machine learning to transform traditional engineering challenges.

Research Focus

Key Achievements

Top Papers

1. 1
   Classification of robotic arm movement using SVM and Naïve Bayes classifiers

   10 citations · 2013

Key Collaborators

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