Abstract
Accurate modeling and control of robotic systems are often critical to perform complex manipulation tasks. Artificial Intelligence (AI) based approaches have gained widespread popularity to deal with task complexity and uncertainty. This paper gives a detailed overview and tutorial of AI-based approaches for modeling and control of robotic systems. We provide an easy-to-understand explanation of the key AI-based techniques and demonstrate learning an inverse dynamics model and target reaching controller for a 2-link robotic manipulator using state-of-the-art AI-based methods. The experimental results present a thorough analysis of AI-based techniques and validate their advantages over conventional methods. The code used for the examples has also been made open-source for the research community on this link: https://www.github.com/ai-modeling-control. Thus, this paper aims to become a starting point for researchers interested in developing and implementing AI-based methods for robot modeling and control.
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