Development of an artificial spinal cord circuit for a musculoskeletal humanoid robot mimicking the neural network involved in human gait control

Artificial neural networks, which mimic the neural networks of living organisms, are being applied as advanced information processing systems in various fields such as robotics. Conventional artificial neural networks use CPUs and software programs, but huge numerical computations are required to imitate a large-scale neural network. On the other hand, hardware artificial neural networks have been proposed. Hardware models neurons and synapses using analog electronic circuits, and thus can mimic the neural signals generated by neural networks without the need for numerical calculations. We have been developing a hardware artificial neural network mimicking the neural network in the human brainstem and spinal cord that is involved in gait control, and applying it to a musculoskeletal humanoid robot that mimics the human musculature and skeletal structure. In this paper, we propose an artificial spinal cord circuit for gait control of a musculoskeletal humanoid robot. Focusing on the movement of stepping over an obstacle, we confirmed through circuit simulations that the artificial spinal cord circuit can generate stepping-over patterns arbitrarily while walking and running.