Chirag Tyagi

About

Chirag Tyagi’s research lies at the intersection of robotics, artificial intelligence, and computational kinematics, with a focus on developing intelligent, learning-based solutions for complex robotic control problems. His most cited work, “Inverse kinematics evaluation for robotic manipulator using support vector regression and kohonen self organizing map” (2016, 5 citations), addresses a fundamental challenge in robotics: the computationally intensive and often non-analytical nature of inverse kinematics for serial manipulators. Tyagi pioneered the use of machine learning techniques—specifically support vector regression and Kohonen self-organizing maps—to deliver faster, more accurate kinematic solutions, bypassing traditional iterative methods. This contribution is particularly valuable for real-time robotic applications where speed and precision are critical. By demonstrating that data-driven models can effectively replace classical numerical approaches, Tyagi’s work has opened new pathways for adaptive and intelligent robotic control. His research continues to influence the design of learning-based systems in automation and robotics, making him a notable figure in the growing field of AI-driven mechanical engineering.

Research Focus

Key Achievements

Top Papers

1. 1
   Inverse kinematics evaluation for robotic manipulator using support vector regression and kohonen self organizing map

   5 citations · 2016

Key Collaborators

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