In the context of robotics and automation, learning from demonstration (LfD) is the paradigm
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …

Generally, structural uncertainty of the robot dynamics system refers to model error caused
by parameter identification, unstructured uncertainty is the unmodeled dynamic …

Model-based control can provide high-accuracy performance over position-based or velocity-
based control. Therefore, to employ model-based control in industrial robots, it is important …

In smart cities and factories, robotic applications require high dexterity and security, which
requires precise inverse dynamics model. However, the physical modeling methods cannot …

In the context of robotics and automation, learning from demonstrations (LfD) is the
paradigm in which robots acquire new skills by learning to imitate an expert. The choice of …

Regardless of their industrial or research application, the streamlining of robot operations is
limited by the proximity of experienced users to the actual hardware. Be it massive open …

Inverse dynamics modeling is a critical problem for the computed-torque control of robotic
manipulator. This paper presents a novel recurrent network based on the modified Simple …

Kinematic estimations and dynamic performance assessments are fundamental theoretical
issues to realize the mechanism from conceptual design to engineering application. In this …

Представлены результаты исследования динамического поведения манипулятора,
которые имеют важное значение для разработки и моделирования его системы …

We transform classical robot inertial parameter identification into an online learning problem
by integrating state-of-the-art gradient descent techniques and first-order principles from …