A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware
This demonstrates a practical application of neuromorphic hardware for autonomous robotics, though it is incremental as it builds on existing deep learning and neuromorphic methods.
They tackled the challenge of implementing neuromorphic computing on a mobile robot by creating a closed-loop system with an IBM TrueNorth NS1e chip, resulting in a self-driving robot that successfully traversed steep mountain trails in real time.
Neuromorphic computing is a promising solution for reducing the size, weight and power of mobile embedded systems. In this paper, we introduce a realization of such a system by creating the first closed-loop battery-powered communication system between an IBM TrueNorth NS1e and an autonomous Android-Based Robotics platform. Using this system, we constructed a dataset of path following behavior by manually driving the Android-Based robot along steep mountain trails and recording video frames from the camera mounted on the robot along with the corresponding motor commands. We used this dataset to train a deep convolutional neural network implemented on the TrueNorth NS1e. The NS1e, which was mounted on the robot and powered by the robot's battery, resulted in a self-driving robot that could successfully traverse a steep mountain path in real time. To our knowledge, this represents the first time the TrueNorth NS1e neuromorphic chip has been embedded on a mobile platform under closed-loop control.