Name: Learning efficient control of non-linear muscle-driven systems: Morphological computation as guiding principle.

Abstract:

Description: <p>Animals generate movement in a fascinatingly efficient, dynamic, and precise way. They achieve this by a well-tuned dynamic interplay between nervous system and muscles where they exploit the visco-elastic properties of the muscles to reduce the neuronal load. This apparent computation performed by the body is termed morphological computation. Building on this idea, novel robotic systems, like muscle driven robots, soft robots, or soft wearable assistive devices are developed. However, the control of non-linear and elastic robotic systems is challenging.</p> <p>In this project, we will employ machine learning approaches to learn a well-tuned dynamic interplay between controller and muscle(-like) actuator. The goal is to explicitly exploit the muscle properties and therefore rely on morphological computation. We will develop this approach with computer simulations of human arm movements which consider muscles and low-level neuronal control (like re exes). We will further add a model of a technical assistive device and learn a controller which helps to maximize the morphological computation in the human neuro-muscular arm model.</p> <p>With our collaboration partners Syn Schmitt (Uni Stuttgart) and Dieter B&uuml;chler (MPIIS), we will also apply this approach to muscle-driven robotic systems. This will allow us to learn a control which also exploits morphological computation in such systems.</p> <p>Learning to exploit morphological computation will provide a novel approach to controlling robotic systems with elastic actuators and soft structures with potential applications especially in human-robot interaction or assistance.</p>

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Meta keywords: Daniel Häufle / Georg Martius

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Slug: lernen-einer-effizienten-steuerung-von-nichtlinearen-muskelgesteuerten-systemen-morphologische-berechnung-als-leitprinzip

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