Toward Brain-Inspired Learning with the Neuromorphic Snake-Like Robot and the Neurorobotic Platform

Neurorobotic mimics the structural and functional principles of living creature systems. Modeling a single system by robotic hardware and software has existed for decades. However, an integrated toolset studying the interaction of all systems has not been demonstrated yet. We present a hybrid neuromorphic computing paradigm to bridge this gap by combining the neurorobotics platform (NRP) with the neuromorphic snake-like robot (NeuroSnake). This paradigm encompasses the virtual models, neuromorphic sensing and computing capabilities, and physical bio-inspired bodies, with which an experimenter can design and execute both in-silico and in-vivo robotic experimentation easily. The NRP is a public Web-based platform for easily testing brain models with virtual bodies and environments. The NeuroSnake is a bio-inspired robot equipped with a silico-retina sensor and neuromorphic computer for power-efficiency applications. We illustrate the efficiencies of our paradigm with an easy designing of a visual pursuit experiment in the NRP. We study two automatic behavior learning tasks which are further integrated into a complex task of semi-autonomous pole climbing. The result shows that robots could build new learning rules in a less explicit manner inspired by living creatures. Our method gives an alternative way to efficiently develop complex behavior control of the ro As spiking neural network is a bio-inspired neural network and the NeuroSnake robot is equipped with a spike-based silicon retina camera, the control system can be easily implemented via spiking neurons simulated on neuromorphic hardware, such as SpiNNaker.bot.