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AMS
2007
Springer

Learning the Inverse Model of the Dynamics of a Robot Leg by Auto-imitation

9 years 6 months ago
Learning the Inverse Model of the Dynamics of a Robot Leg by Auto-imitation
Abstract Walking, running and hopping are based on self-stabilizing oscillatory activity. In contrast, aiming movements serve to direct a limb to a desired location and demand a quite different manner of control which also includes learning the physical parameters of that limb. The paper is concerned with the question how reaching a goal can be integrated into locomotion. A prerequisite of piecing together both types of control is the acquisition of a model of the limb’s inverse dynamics. To test whether auto-imitation, a biologically inspired learning algorithm, can solve this problem, we build a motor driven device with a two-segmented arm. A preliminary study revealed that at least the forearm - with the upper arm fixed - can be made controllable by this method we called ”autoimitatively adaptable inverse control”.
Karl-Theodor Kalveram, André Seyfarth
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where AMS
Authors Karl-Theodor Kalveram, André Seyfarth
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