Musculoskeletal Robots: Scalability in Neural Control Richter, Christoph Jentzsch, Sören Hostettler, Rafael Garrido Alcázar, Jesús Alberto Ros Vidal, Eduardo Knoll, Alois C. Röhrbein, Florian Smagt, Patrick van der Conradt, Jörg 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. 2024-10-25T07:28:45Z 2024-10-25T07:28:45Z 2016-08-26 journal article C. Richter et al., "Musculoskeletal Robots: Scalability in Neural Control," in IEEE Robotics & Automation Magazine, vol. 23, no. 4, pp. 128-137, Dec. 2016, doi: 10.1109/MRA.2016.2535081 https://hdl.handle.net/10481/96344 10.1109/MRA.2016.2535081 eng info:eu-repo/grantAgreement/EC/FP7/604102 info:eu-repo/grantAgreement/EC/FP7/288219 info:eu-repo/grantAgreement/EC/H2020/MSC 653019 http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Institute of Electrical and Electronics Engineers (IEEE)