  Anthropomimetic robots are robots that sense, behave, interact and feel like
humans. By this definition, anthropomimetic robots 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 been demonstrated yet. 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 which
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 naturally be
incorporated.
