Norbert Michael Mayer

Chun-Chi Wang, He-Zhi Liu, Rui-Yuan Lin et al.

In this paper, we describe features of two new robot prototypes that are actuated by an actively controlled gyro (flywheel, symmetric rotor) inside a hollow sphere that is located in the middle of the robots. No external actuators are used. The outside structure of the robots and the gyro are connected by a gimbal, which is similar in structure to a control moment gyroscope in spacecrafts. The joints of the gimbal can be actuated. In this way, the orientation of axis for the gyro in relation to the egg can be changed. Since the inertia of the fast rotating gyro is large in relation to the outside structure, a relative rotation of the axis against the outside structure results in a motion of the egg by inertia principle. In this way, we can use this principle for controlling the robot to move forward and turn around. The robots are shaped as spheroidal ellipsoids so they resemble eggs. So far, we have built and tested two robot prototypes.

Norbert Michael Mayer

The manuscript discusses still preliminary considerations with regard to the dynamics and kinematics of an egg shaped robot with an gyro driven inertia actuator. The method of calculation follows the idea that we would like to express the entire dynamic equations in terms of moments instead of forces. Also we avoid to derive the equations from a Lagrange function with constraints. The result of the calculations is meant to be applicable to two robot prototypes that have been build at the AES\&R Laboratory at the National Chung Cheng University in Taiwan.

Norbert Michael Mayer

The paper investigates a new type of truly critical echo state networks where individual transfer functions for every neuron can be modified to anticipate the expected next input. Deviations from expected input are only forgotten slowly in power law fashion. The paper outlines the theory, numerically analyzes a one neuron model network and finally discusses technical and also biological implications of this type of approach.

Norbert Michael Mayer

Usually, reservoir computing shows an exponential memory decay. This paper investigates under which circumstances echo state networks can show a power law forgetting. That means traces of earlier events can be found in the reservoir for very long time spans. Such a setting requires critical connectivity exactly at the limit of what is permissible according the echo state condition. However, for general matrices the limit cannot be determined exactly from theory. In addition, the behavior of the network is strongly influenced by the input flow. Results are presented that use certain types of restricted recurrent connectivity and anticipation learning with regard to the input, where indeed power law forgetting can be achieved.