Embodied Neuromorphic Vision with Continuous Random Backpropagation

Jacques Kaiser, Alexander Friedrich, Juan Camilo Vasquez Tieck, Daniel Reichard, Arne Roennau, Emre Neftci, Rüdiger Dillmann

Abstract: The brain outperforms computer architectures in aspects of energy efficiency, robustness and adaptivity. Brain computations are modeled in silico with spiking neural networks and neuromorphic hardware. Recently, three-factor synaptic plasticity rules approximating backpropagation have been derived. Suited to neuromorphic hardware, these rules can learn online with asynchronous updates. In this paper, we present Continuous Random Backpropagation (cRBP), a continuous version of Event-Driven Random Backpropagation. This learning rule performs comparably to state-of-the-art rules on the DvsGesture dataset. We additionally show that the accuracy can be significantly increased with a simple attention mechanism. This mechanism provides translation invariance at low computational cost compared to convolutions by exploiting event stream sparsity. Subsequently, we integrate cRBP in a real robotic setup, where a gripper grasps objects according to the detected visual affordances. In this setup, visual information is actively sensed by a Dynamic Vision Sensor (DVS) mounted on a robotic head performing microsaccadic eye movements. Our results suggest that advances in neuromorphic technology and plasticity rules enable the development of learning robots operating at high speed and low power.