Header logo is am

7 results (View BibTeX file of all listed publications)

2002

no image
Learning robot control

Schaal, S.

In The handbook of brain theory and neural networks, 2nd Edition, pages: 983-987, 2, (Editors: Arbib, M. A.), MIT Press, Cambridge, MA, 2002, clmc (inbook)

Abstract
This is a review article on learning control in robots.

link (url) [BibTex]

2002

Schaal, S. Learning robot control In The handbook of brain theory and neural networks, 2nd Edition, pages: 983-987, 2, (Editors: Arbib, M. A.), MIT Press, Cambridge, MA, 2002, clmc (inbook)

link (url) [BibTex]

no image
Arm and hand movement control

Schaal, S.

In The handbook of brain theory and neural networks, 2nd Edition, pages: 110-113, 2, (Editors: Arbib, M. A.), MIT Press, Cambridge, MA, 2002, clmc (inbook)

Abstract
This is a review article on computational and biological research on arm and hand control.

link (url) [BibTex]

Schaal, S. Arm and hand movement control In The handbook of brain theory and neural networks, 2nd Edition, pages: 110-113, 2, (Editors: Arbib, M. A.), MIT Press, Cambridge, MA, 2002, clmc (inbook)

link (url) [BibTex]

2000

no image
Biomimetic gaze stabilization

Shibata, T., Schaal, S.

In Robot learning: an Interdisciplinary approach, pages: 31-52, (Editors: Demiris, J.;Birk, A.), World Scientific, 2000, clmc (inbook)

Abstract
Accurate oculomotor control is one of the essential pre-requisites for successful visuomotor coordination. In this paper, we suggest a biologically inspired control system for learning gaze stabilization with a biomimetic robotic oculomotor system. In a stepwise fashion, we develop a control circuit for the vestibulo-ocular reflex (VOR) and the opto-kinetic response (OKR), and add a nonlinear learning network to allow adaptivity. We discuss the parallels and differences of our system with biological oculomotor control and suggest solutions how to deal with nonlinearities and time delays in the control system. In simulation and actual robot studies, we demonstrate that our system can learn gaze stabilization in real time in only a few seconds with high final accuracy.

link (url) [BibTex]

2000

Shibata, T., Schaal, S. Biomimetic gaze stabilization In Robot learning: an Interdisciplinary approach, pages: 31-52, (Editors: Demiris, J.;Birk, A.), World Scientific, 2000, clmc (inbook)

link (url) [BibTex]

1999

no image
Nonparametric regression for learning nonlinear transformations

Schaal, S.

In Prerational Intelligence in Strategies, High-Level Processes and Collective Behavior, 2, pages: 595-621, (Editors: Ritter, H.;Cruse, H.;Dean, J.), Kluwer Academic Publishers, 1999, clmc (inbook)

Abstract
Information processing in animals and artificial movement systems consists of a series of transformations that map sensory signals to intermediate representations, and finally to motor commands. Given the physical and neuroanatomical differences between individuals and the need for plasticity during development, it is highly likely that such transformations are learned rather than pre-programmed by evolution. Such self-organizing processes, capable of discovering nonlinear dependencies between different groups of signals, are one essential part of prerational intelligence. While neural network algorithms seem to be the natural choice when searching for solutions for learning transformations, this paper will take a more careful look at which types of neural networks are actually suited for the requirements of an autonomous learning system. The approach that we will pursue is guided by recent developments in learning theory that have linked neural network learning to well established statistical theories. In particular, this new statistical understanding has given rise to the development of neural network systems that are directly based on statistical methods. One family of such methods stems from nonparametric regression. This paper will compare nonparametric learning with the more widely used parametric counterparts in a non technical fashion, and investigate how these two families differ in their properties and their applicabilities. We will argue that nonparametric neural networks offer a set of characteristics that make them a very promising candidate for on-line learning in autonomous system.

link (url) [BibTex]

1999

Schaal, S. Nonparametric regression for learning nonlinear transformations In Prerational Intelligence in Strategies, High-Level Processes and Collective Behavior, 2, pages: 595-621, (Editors: Ritter, H.;Cruse, H.;Dean, J.), Kluwer Academic Publishers, 1999, clmc (inbook)

link (url) [BibTex]

1993

no image
Learning passive motor control strategies with genetic algorithms

Schaal, S., Sternad, D.

In 1992 Lectures in complex systems, pages: 913-918, (Editors: Nadel, L.;Stein, D.), Addison-Wesley, Redwood City, CA, 1993, clmc (inbook)

Abstract
This study investigates learning passive motor control strategies. Passive control is understood as control without active error correction; the movement is stabilized by particular properties of the controlling dynamics. We analyze the task of juggling a ball on a racket. An approximation to the optimal solution of the task is derived by means of optimization theory. In order to model the learning process, the problem is coded for a genetic algorithm in representations without sensory or with sensory information. For all representations the genetic algorithm is able to find passive control strategies, but learning speed and the quality of the outcome are significantly different. A comparison with data from human subjects shows that humans seem to apply yet different movement strategies to the ones proposed. For the feedback representation some implications arise for learning from demonstration.

link (url) [BibTex]

1993

Schaal, S., Sternad, D. Learning passive motor control strategies with genetic algorithms In 1992 Lectures in complex systems, pages: 913-918, (Editors: Nadel, L.;Stein, D.), Addison-Wesley, Redwood City, CA, 1993, clmc (inbook)

link (url) [BibTex]

no image
A genetic algorithm for evolution from an ecological perspective

Sternad, D., Schaal, S.

In 1992 Lectures in Complex Systems, pages: 223-231, (Editors: Nadel, L.;Stein, D.), Addison-Wesley, Redwood City, CA, 1993, clmc (inbook)

Abstract
In the population model presented, an evolutionary dynamic is explored which is based on the operator characteristics of genetic algorithms. An essential modification in the genetic algorithms is the inclusion of a constraint in the mixing of the gene pool. The pairing for the crossover is governed by a selection principle based on a complementarity criterion derived from the theoretical tenet of perception-action (P-A) mutuality of ecological psychology. According to Swenson and Turvey [37] P-A mutuality underlies evolution and is an integral part of its thermodynamics. The present simulation tested the contribution of P-A-cycles in evolutionary dynamics. A numerical experiment compares the population's evolution with and without this intentional component. The effect is measured in the difference of the rate of energy dissipation, as well as in three operationalized aspects of complexity. The results support the predicted increase in the rate of energy dissipation, paralleled by an increase in the average heterogeneity of the population. Furthermore, the spatio-temporal evolution of the system is tested for the characteristic power-law relations of a nonlinear system poised in a critical state. The frequency distribution of consecutive increases in population size shows a significantly different exponent in functional relationship.

[BibTex]

Sternad, D., Schaal, S. A genetic algorithm for evolution from an ecological perspective In 1992 Lectures in Complex Systems, pages: 223-231, (Editors: Nadel, L.;Stein, D.), Addison-Wesley, Redwood City, CA, 1993, clmc (inbook)

[BibTex]