Deep spiking networks for model-based planning in humanoids

We propose a novel bioinspired motion planning approach based on deep networks. This Deep Spiking Network (DSN) architecture couples task and joint space planning through bidirectional feedback. We show that the DSN can learn arbitrary complex functions, encode forward and inverse models, generate different solutions simultaneously and adapt dynamically to changing task constraints or environments. Furthermore, to scale to high-dimensional spaces, we introduce a factorized population coding in the model. Moreover, we show that the DSN can be trained efficiently and exclusively from human demonstrations to learn a task independent and reusable planning model. The model is evaluated in simulation and on two real high-dimensional humanoid robotic systems.