  Adversarial training (i.e., training on adversarially perturbed input data)
is a well-studied method for making neural networks robust to potential
adversarial attacks during inference. However, the improved robustness does not
come for free but rather is accompanied by a decrease in overall model accuracy
and performance. Recent work has shown that, in practical robot learning
applications, the effects of adversarial training do not pose a fair trade-off
but inflict a net loss when measured in holistic robot performance. This work
revisits the robustness-accuracy trade-off in robot learning by systematically
analyzing if recent advances in robust training methods and theory in
conjunction with adversarial robot learning, are capable of making adversarial
training suitable for real-world robot applications. We evaluate three
different robot learning tasks ranging from autonomous driving in a
high-fidelity environment amenable to sim-to-real deployment to mobile robot
navigation and gesture recognition. Our results demonstrate that, while these
techniques make incremental improvements on the trade-off on a relative scale,
the negative impact on the nominal accuracy caused by adversarial training
still outweighs the improved robustness by an order of magnitude. We conclude
that although progress is happening, further advances in robust learning
methods are necessary before they can benefit robot learning tasks in practice.
