Towards a framework for end-to-end control of a simulated vehicle with spiking neural networks

• Published in: 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR) pp. 127 - 134
• Main Authors: Kaiser, Jacques, Vasquez Tieck, J. Camilo, Hubschneider, Christian, Wolf, Peter, Weber, Michael, Hoff, Michael, Friedrich, Alexander, Wojtasik, Konrad, Roennau, Arne, Kohlhaas, Ralf, Dillmann, Rudiger, Zollner, J. Marius
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2016
• Subjects: Biological neural networks; Brain modeling; Cameras; Robot sensing systems; Voltage control
• DOI: 10.1109/SIMPAR.2016.7862386

Spiking neural networks are in theory more computationally powerful than rate-based neural networks often used in deep learning architectures. However, unlike rate-based neural networks, it is yet unclear how to train spiking networks to solve complex problems. There are still no standard algorithms and it is preventing roboticists to use spiking networks, yielding a lack of Neurorobotics applications. The contribution of this paper is twofold. First, we present a modular framework to evaluate neural self-driving vehicle applications. It provides a visual encoder from camera images to spikes inspired by the silicon retina (DVS), and a steering wheel decoder based on an agonist antagonist muscle model. Secondly, using this framework, we demonstrate a spiking neural network which controls a vehicle end-to-end for lane following behavior. The network is feed-forward and relies on hand-crafted feature detectors. In future work, this framework could be used to design more complex networks and use the evaluation metrics for learning.