Echo State Networks and Liquid State Machines introduced a new paradigm in artificial
recurrent neural network (RNN) training, where an RNN (the reservoir) is generated …

Abstract Models are among the most essential tools in robotics, such as kinematics and
dynamics models of the robot's own body and controllable external objects. It is widely …

The human brain is a dynamical system whose extremely complex sensor-driven neural
processes give rise to conceptual, logical cognition. Understanding the interplay between …

Biological brains can learn, recognize, organize, and re-generate large repertoires of
temporal patterns. Here I propose a mechanism of neurodynamical pattern learning and …

This paper investigates the influence of different joint space and orientation representations
on the approximation of the forward kinematics. We consider all degrees of freedom in three …

Abstract Machine learning offers to robotics a framework and set of tools for the design of
sophisticated and hard-to-engineer behaviors; conversely, the challenges of robotic …

We present a dynamical system approach that couples task and joint space by means of an
attractor-based content addressable memory. The respective recurrent reservoir network …

Output feedback is crucial for autonomous and parameterized pattern generation with
reservoir networks. Read-out learning affects the output feedback loop and can lead to error …

Inverse kinematics is an important basic theory in walking control of biped robot. This study
focuses on the parameter setting using the improved algorithm in inverse kinematics. By …

Dynamical systems which generate periodic signals are of interest as models of biological
central pattern generators and in a number of robotic applications. A basic functionality that …