Journal article
Christoph Richter, Soren Jentzsch, Rafael Hostettler, J. Garrido, E. Vidal, A. Knoll, Florian Röhrbein, Patrick van der Smagt, J. Conradt
IEEE robotics & automation magazine, 2016
IEEE robotics & automation magazine, 2016
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APA
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Richter, C., Jentzsch, S., Hostettler, R., Garrido, J., Vidal, E., Knoll, A., … Conradt, J. (2016). Musculoskeletal Robots: Scalability in Neural Control. IEEE Robotics &Amp; Automation Magazine.
Chicago/Turabian
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Richter, Christoph, Soren Jentzsch, Rafael Hostettler, J. Garrido, E. Vidal, A. Knoll, Florian Röhrbein, Patrick van der Smagt, and J. Conradt. “Musculoskeletal Robots: Scalability in Neural Control.” IEEE robotics & automation magazine (2016).
MLA
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Richter, Christoph, et al. “Musculoskeletal Robots: Scalability in Neural Control.” IEEE Robotics &Amp; Automation Magazine, 2016.
BibTeX Click to copy
@article{christoph2016a,
title = {Musculoskeletal Robots: Scalability in Neural Control},
year = {2016},
journal = {IEEE robotics & automation magazine},
author = {Richter, Christoph and Jentzsch, Soren and Hostettler, Rafael and Garrido, J. and Vidal, E. and Knoll, A. and Röhrbein, Florian and van der Smagt, Patrick and Conradt, J.}
}
Abstract
Anthropomimetic robots sense, behave, interact, and feel like humans. By this definition, they require human-like physical hardware and actuation but also brain-like control and sensing. The most self-evident realization to meet those requirements would be a human-like musculoskeletal robot with a brain-like neural controller. While both musculoskeletal robotic hardware and neural control software have existed for decades, a scalable approach that could be used to build and control an anthropomimetic human-scale robot has not yet been demonstrated. Combining Myorobotics, a framework for musculoskeletal robot development, with SpiNNaker, a neuromorphic computing platform, we present the proof of principle of a system that can scale to dozens of neurally controlled, physically compliant joints. At its core, it implements a closed-loop cerebellar model that provides real-time, low-level, neural control at minimal power consumption and maximal extensibility. Higher-order (e.g., cortical) neural networks and neuromorphic sensors like silicon retinae or cochleae can be incorporated.