  As a result of increasingly adopted machine learning algorithms and
ubiquitous sensors, many 'perception-to-control' systems are developed and
deployed. For these systems to be trustworthy, we need to improve their
robustness with adversarial training being one approach. We propose a
gradient-free adversarial training technique, called AutoJoin, which is a very
simple yet effective and efficient approach to produce robust models for
imaged-based maneuvering. Compared to other SOTA methods with testing on over
5M perturbed and clean images, AutoJoin achieves significant performance
increases up to the 40% range under gradient-free perturbations while improving
on clean performance up to 300%. Regarding efficiency, AutoJoin demonstrates
strong advantages over other SOTA techniques by saving up to 83% time per
training epoch and 90% training data. Although not the focus of AutoJoin, it
even demonstrates superb ability in defending gradient-based attacks. The core
idea of AutoJoin is to use a decoder attachment to the original regression
model creating a denoising autoencoder within the architecture. This
architecture allows the tasks 'maneuvering' and 'denoising sensor input' to be
jointly learnt and reinforce each other's performance.
