Skip to main content Skip to main navigation


Deep spiking networks for model-based planning in humanoids

Daniel Tanneberg; Alexandros Paraschos; Jan Peters; Elmar Rueckert
In: 16th IEEE-RAS International Conference on Humanoid Robots. IEEE-RAS International Conference on Humanoid Robots (Humanoids-2016), November 15-17, Cancun, Mexico, Pages 656-661, IEEE, 2016.


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.

Weitere Links