Scalability in Neural Control of Musculoskeletal Robots
This work addresses the scalability problem for researchers and engineers building anthropomimetic robots, though it is incremental as it builds on existing hardware and software components.
The authors tackled the challenge of creating a scalable neural control system for human-like musculoskeletal robots by combining the Myorobotics framework with the SpiNNaker neuromorphic platform, achieving a proof-of-principle system that can control dozens of joints with real-time, low-power operation.
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.