Accurate modeling and control of robotic systems are often critical to perform complex
manipulation tasks. Artificial Intelligence (AI) based approaches have gained widespread …

As a representative cyber-physical system (CPS), robotic manipulators have been widely
adopted in various academic research and industrial processes, indicating their potential to …

The dominant way to control a robot manipulator uses hand-crafted differential equations
leveraging some form of inverse kinematics/dynamics. We propose a simple, versatile joint …

This work presents an approach for control, state-estimation and learning model (hyper)
parameters for robotic manipulators. It is based on the active inference framework …

In this paper, we focus on enhancing the performance of the controller utilized in the Stewart
platform by investigating the dynamics of the platform. Dynamic modeling is crucial for …

The inverse dynamics of a robotic manipulator is instrumental in precise robot control and
manipulation. However, acquiring such a model is challenging, not only due to unmodelled …

Active inference is a mathematical framework that originated in computational neuroscience.
Recently, it has been demonstrated as a promising approach for constructing goal-driven …

Active inference, a theoretical construct inspired by brain processing, is a promising
alternative to control artificial agents. However, current methods do not yet scale to high …

Due to the approximation errors in dynamic model, changing payloads and dynamic
disturbances acting on the system, the model based tracking is not satisfactory. Hence the …

In this paper, an active model-based control scheme is proposed for the manipulators with
parameter uncertainties and disturbances. Firstly the Euler-Lagrange dynamic model of n …