Hareesh Singanamala

About

Hareesh Singanamala is a robotics researcher whose work sits at the intersection of machine learning and kinematic control. His primary research focus is on transforming traditional robotic arm articulation—typically governed by forward and inverse kinematics—into data-driven classification problems. Singanamala’s major contribution lies in demonstrating that machine learning models can effectively predict and classify movement patterns in 3D space, offering an alternative to rigid, target-oriented approaches. His most cited work, "Classifying Movement Articulation for Robotic Arms via Machine Learning" (2013, 12 citations), pioneered this shift by treating motor articulation as a learnable task. He further validated this approach in "Classification of robotic arm movement using SVM and Naïve Bayes classifiers" (2013, 10 citations), showing that both support vector machines and probabilistic classifiers can reliably predict arm movements after prior training. Though his citation counts are modest, Singanamala’s work is notable for its early adoption of classification algorithms in physical robotics—a precursor to today’s learning-based control systems. His research remains a touchstone for engineers seeking to replace deterministic kinematic chains with adaptive, data-driven motion planning.

Research Focus

Key Achievements

Top Papers

1. 1
   Classifying Movement Articulation for Robotic Arms via Machine Learning

   12 citations · 2013
2. 2
   Classification of robotic arm movement using SVM and Naïve Bayes classifiers

   10 citations · 2013

Key Collaborators

Contact & Links