Trainable sensorimotor mapping in a neuromorphic robot

• We present a mobile omniwheel robot capable of running large-scale neural models.
• We demonstrate a software suite for running neural models on the robot.
• We demonstrate using this suite to learn complex parallel sensorimotor mappings.

We present a mobile robot with sufficient computing power to simulate up to a quarter of a million neurons in real-time. We use this computing power, combined with various on-board sensory and motor systems (including silicon retinae) to implement a novel method for learning sensorimotor competences by example. That is, by temporarily manually controlling the robot, it can gather information about what sensorimotor mapping it should be performing. We show that such a learning-by-example system is well-suited to power efficient neuron-based computation (60 W for all quarter of a million neurons), that it can learn quickly (a few tens of seconds), and that its learning generalizes well to novel situations.