Frank Hesse

Rocking Stamper and Jumping Snakes from a Dynamical Systems Approach to Artificial Life

Dynamical systems offer intriguing possibilities as a substrate for the generation of behavior because of their rich behavioral complexity. However this complexity together with the largely covert relation between the parameters and the behavior of the agent is also the main hindrance in the goal oriented design of a behavior system. This paper presents a general approach to the self-regulation of dynamical systems so that the design problem is circumvented. We consider the controller (a neural net work) as the mediator for changes in the sensor values over time and define a dynamics for the parameters of the controller by maximizing the dynamical complexity of the sensorimotor loop under the condition that the consequences of the actions taken are still predictable. This very general principle is given a concrete mathematical formulation and is implemented in an extremely robust and versatile algorithm for the parameter dynamics of the controller. We consider two different applications, a mechanical device called the rocking stamper and the ODE simulations of a “snake” with five degrees of freedom. In these and many other examples studied we observed various behavior modes of high dynamical complexity

Biologically inspired reactive climbing behavior of hexapod robots

Insects, e.g. cockroaches and stick insects, have found fascinating solutions for the problem of locomotion, especially climbing over a large variety of obstacles. Research on behavioral neurobiology has identified key behavioral patterns of these animals (i.e., body flexion, center of mass elevation, and local leg reflexes) necessary for climbing. Inspired by this finding, we develop a neural control mechanism for hexapod robots which generates basic walking behavior and especially enables them to effectively perform reactive climbing behavior. The mechanism is composed of three main neural circuits: locomotion control, reactive backbone joint control, and local leg reflex control. It was developed and tested using a physical simulation environment, and was then successfully transferred to a physical six-legged walking machine, called AMOS II. Experimental results show that the controller allows the robot to overcome obstacles of various heights (e.g., ~ 75% of its leg length, which are higher than those that other comparable legged robots have achieved so far). The generated climbing behavior is also comparable to the one observed in cockroaches.

A Sensor-Based Learning Algorithm for the Self-Organization of Robot Behavior

Author to whom correspondence should be addressed.Received: 30 November 2008 / Accepted: 26 February 2009 / Published: 4 March 2009Abstract: Ideally, sensory information forms the only source of information to a robot. Weconsider an algorithm for the self-organization of a controller. At short time scales the con-troller is merely reactive but the parameter dynamics and the acquisition of knowledge by aninternal model lead to seemingly purposeful behavior on longer time scales. As a paradigmaticexample, we study the simulation of an underactuated snake-like robot. By interacting withthe real physical system formed by the robotic hardware and the environment, the controllerachieves a sensitive and body-specific actuation of the robot.Keywords: Self-Organization; Autonomous Robot Control; Neural Networks; Homeokine-sis.

MODULATED EXPLORATORY DYNAMICS CAN SHAPE SELF-ORGANIZED BEHAVIOR

We study an adaptive controller that adjusts its internal parameters by self-organization of its interaction with the environment. We show that the parameter changes that occur in this low-level learning process can themselves provide a source of information to a higher-level context-sensitive learning mechanism. In this way, the context is interpreted in terms of the concurrent low-level learning mechanism. The dual learning architecture is studied in realistic simulations of a foraging robot and of a humanoid hand that manipulated an object. Both systems are driven by the same low-level scheme, but use the second-order information in different ways. While the low-level adaptation continues to follow a set of rigid learning rules, the second-order learning modulates the elementary behaviors and affects the distribution of the sensory inputs via the environment.

Homeokinetic proportional control of myoelectric prostheses

Self-organized control of myoelectric prostheses aims at an automatic selection of communication channels between a prosthetic device and its user. During training, the patient is instructed to generate control signals that follow the observed autonomous movements of the prosthesis. At the same time, the prosthetic controller maximizes both the diversity of movements and the coincidences of prosthetic movements and human control signals by varying the sensory features and control actions. This dual control algorithm is derived from the homeokinetic principle for robot control and is tested in a proportional control task for a hand prostheses.

A GOAL-ORIENTATION FRAMEWORK FOR SELF-ORGANIZING CONTROL

Self-organization, especially in the framework of embodiment in biologically inspired robots, allows the acquisition of behavioral primitives by autonomous robots themselves. However, it is an open question how self-organization of basic motor primitives and goal-orientation can be combined, which is a prerequisite for the usefulness of such systems. In the paper at hand we propose a goal-orientation framework allowing the combination of self-organization and goal-orientation for the control of autonomous robots in a mutually independent fashion. Self-organization based motor primitives are employed to achieve a given goal. This requires less initial knowledge about the properties of robot and environment and increases adaptivity of the overall system. A combination of self-organization and reward-based learning seems thus a promising route for the development of adaptive learning systems.

Homeokinetic prosthetic control: collaborative selection of myosignal features

We present an approach to the control of myoelectric prostheses that is based on a collaborative interaction of a prosthesis and the patient. During training, the patient is instructed to generate control signals that follow the observed autonomous movements of the prosthesis. At the same time, the prosthetic controller maximizes both the diversity of movements and the coincidences of prosthetic movements and human control signals by varying the features and control actions. This dual control principle which is derived from the homeokinetic robot control is demonstrated to be efficient for the control of a hand prostheses with two degrees of freedom.

Structured control from self-organizing arm movements

Introduction The organization of unconstrained arm movements in humans appears to be determined essentially by the biophysical properties of the limb [1], which, however, might imply as well that the underlying neural control mechanisms are perfectly adapted to controlled system. The stiffness of the arm with respect to perturbing forces [2] is caused by an active process that cannot be inferred from the biomechanics of the arm alone, and may thus reveal information about the underlying neural control mechanisms. We approach the problem by analyzing experimental measurements of stiffness in human subjects and by simulations of emergent control of a model of the human arm.