3 accepted papers
Inverse dynamics models are applied to a plethora of robot control tasks such as computed-torque control, which are essential for trajectory execution. The analytical derivation of such dynamics models for robotic manipulators can be challenging and depends on their physical characteristics. This pa…
Many robot learning algorithms depend on a model of the robot's forward dynamics for simulating potential trajectories and ultimately learning a required task. In this paper, we present a data-driven reservoir computing approach and apply it for learning forward dynamics models. Our proposed machine…
In certain cases analytical derivation of physics-based models of robots is difficult or even impossible. A potential workaround is the approximation of robot models from sensor data-streams employing machine learning approaches. In this paper, the inverse dynamics models are learned by employing a…