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Open AccePoster presentationStructured control from self-organizing arm movementsKatja Fiedler*1, Georg Martius1,2, Frank Hesse1,2 and J Michael Herrmann1,2,3

Address: 1MPI for Dynamics and Self-Organization, Goettingen, Bunsenstr. 10, 37073 Goettingen, Germany, 2Bernstein Center for Computational Neuroscience Goettingen, Bunsenstr. 10, 37073 Goettingen, Germany and 3Inst. of Perception, Action and Behaviour, School of Informatics, University of Edinburgh, EH9 3JZ, UK

Email: J Michael Herrmann - michael.herrmann@ed.ac.uk

* Corresponding author

IntroductionThe organization of unconstrained arm movements inhumans appears to be determined essentially by the bio-physical properties of the limb [1], which, however, mightimply as well that the underlying neural control mecha-nisms are perfectly adapted to controlled system. The stiff-ness of the arm with respect to perturbing forces [2] iscaused by an active process that cannot be inferred fromthe biomechanics of the arm alone, and may thus revealinformation about the underlying neural control mecha-nisms. We approach the problem by analyzing experi-mental measurements of stiffness in human subjects andby simulations of emergent control of a model of thehuman arm.

Physical simulations of a controlled human armControl is achieved by an algorithm for self-organizingcontrol of sensorimotor loops [3] that establishes adynamical system in the sensor space. The resultingdynamics is characterized by both the sensitive depend-ence on previous actions as well as the local predictabilityby an internal model. The algorithm merely follows theobjective of conveying the sensorimotor loop into a mar-ginally stable regime, which has been shown to suffice togenerate a variety of behaviors in different robots [3]. Herewe study the example of a four DOF model of a humanarm. The controller creates a manifold of behaviors by anitinerant motion across the critical region of its parameterspace. In order to identify "natural" behaviors a set of con-trollers is used. An effective controller can be selected bymaximal learning progress [4] and upon convergence to alocal optimum the active controller is stored as elemen-

tary behaviors in a premotor layer and can be reactivatedby environmental affordances or by high-level planning.

Experiments on arm movements in human subjectsIn parallel we have studied properties of human armmovements in a four-DOF setting (wrist-restrained) inthree spatial dimensions using a high-performance hapticdevice. We have measured the stiffness based on the equi-librium point hypothesis by the motor response to a per-turbing force and in dependence on the spatial position ofthe arm. In a second set of experiments we have studiedtrajectories in a self-referential task, where the subjectschase a target which is driven by a fast-learning controller.The controller learns to predict the subject's movementsuch that an increasing difficulty of the task is achievedand more complex movements can be expected.

ResultsWhile at low complexity in the chasing task closed-loopcontrol is sufficient, the subjects become more likely touse preexisting open-loop movement primitives at anincreased task complexity. The primitives are extractedfrom the data by third-order statistics and share geometri-cal properties with the primitives that are produced byself-organizing multi-agent control of the simulated arm.The measurement of the stiffness allows us to derive acommon explanation for the control-related aspects of themovement primitives in both experiment and simulation.Furthermore, the spatial heterogeneity of the extent anddensity of the primitives can be related to the stiffness esti-mates. Finally, we discuss application to prosthetics and

from Seventeenth Annual Computational Neuroscience Meeting: CNS*2008Portland, OR, USA. 19–24 July 2008

Published: 11 July 2008

BMC Neuroscience 2008, 9(Suppl 1):P74 doi:10.1186/1471-2202-9-S1-P74

This abstract is available from: http://www.biomedcentral.com/1471-2202/9/S1/P74

© 2008 Fiedler et al; licensee BioMed Central Ltd.

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suggest as a principle for the organization of the workingrange of arm movements that the entire sensorimotorspace is partitioned into segments where the associatedelementary behavior assumes optimal controllability.

AcknowledgementsThe authors thank J. Schroeder-Schetelig for assistance. K.F. has received support by the "Compositionality" project (DIP-F1.2), G.M. and F.H. have received support by BCCNGoe grant #01GQ0432

References1. Biess A, Liebermann DG, Flash T: A computational model for

redundant human 3D pointing movements. Journal of Neuro-science 2007, 27(48):13045-13064.

2. Flash T, Mussa-Ivaldi F: Human arm stiffness characteristicsduring the maintenance of posture. Experimental Brain Research1990, 82:315-326.

3. . http://robot.informatik.uni-leipzig.de/research/publications, http://robot.informatik.uni-leipzig.de/research/videos

4. Herrmann JM: Dynamical Systems for Predictive Control ofAutonomous Robots. Theory in Biosciences 2001, 120(3–4):241-252.

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